Picture the difference between a "drug" and a true "product". Hidden in that distinction is the reason so many promising therapies never reach patients. It’s not just about discovery or process—it’s about stitching together everything from regulatory wisdom to the practical realities of manufacturing, and doing it early enough to matter.
Meet Milan Tomic, a scientist whose career defies a straight line. With three decades in biotech, five antibody products shepherded from DNA to phase one, and hard-won insights building a 2,000-liter GMP manufacturing facility, Milan brings the perspective of someone who’s turned scientific curiosity into operational muscle. Now the founder of Albrem Biopharma, he’s uniquely positioned at the intersection of science, business, and execution.
One of the key aspects that I would put squarely in this area is the involvement of both Regulatory Affairs and Medical Affairs. At a very early point in drug development, when I make the distinction between, "oh, here's a drug and here's a product", our desire would be to make products that are effective, that are helpful, that are meaningful in terms of patients and conditions or treatment of patients. But really understanding how to link the science to the application of the drug and where it fills the gap in what is necessary is important in the design of your drug and the design of your drug's development process.
David Brühlmann [00:00:57]:
What does it take to bring a life-saving therapy from a research idea all the way to a patient's bedside? Few people have stood at more points along that path than Milan Tomic. He has spent 30 years in biotechnology. He developed five antibody co-mixture products from DNA through Phase 1 and worked on a 2,000-liter manufacturing facility and brought that up to EU IMP certification. Today he is building Albrem, his own company. In part one, we explore the career decisions, the pivots, and hard-won lessons that shaped that journey. Welcome, Milan, to the show. It's good to have you on today.
Milan Tomic [00:02:53]:
Thank you. I'm glad to be here. I'm looking forward to this chat.
David Brühlmann [00:02:59]:
To get us going, Milan, let me ask you this perhaps controversial question. Share something that you believe about bioprocess development that most people disagree with.
Milan Tomic [00:03:10]:
It's a very good question, and one that I had to think back through — what I have done, really where I am now, and how I approach things. I think one of the key aspects that I would put squarely in this area is the involvement of both Regulatory Affairs and Medical Affairs at a very early point in drug development. And this puts you really at a point post-discovery, when you know some of the basics of the drug and it looks like it could work. Getting an idea of really how this can be affected during the translation of your drug into an actual product is very critical. And I make the distinction between, "Oh, here's a drug and here's a product".
Our desire would be to make products that are effective, that are helpful, that are meaningful in terms of patients and conditions or treatment of patients. But really understanding how to link the science to the application of the drug and where it fills the gap in what is necessary is important in the design of your drug and the design of your drug's development process. It's one of the key aspects, I believe. What I have seen is that a lot of people put Regulatory Affairs into the compliance section, into Quality Control, or such. I do believe that both Regulatory Affairs and Medical Affairs belong more in development, more in the strategy of developing your drug for the market.
David Brühlmann [00:05:02]:
Milan, you've got an exciting career story, an exciting journey. Take us back to the beginning. What first drew you into molecular biology and biochemistry? And what were some choices that you took from the bench into increasingly senior operational and leadership roles?
Milan Tomic [00:05:24]:
It's not quite a random walk, but it is sort of, I think, if you want a catchphrase for it, a “connect the dots” kind of exploration. I really developed a curiosity about how things worked and then pursued that. And if you look at it that way, not always will you have your own path in a career or your own selection of things to do. But there are lots of things that are interesting in the overall direction that you would like to grow your scientific understanding or even your career and your personal advancement in.
And so the path in this case was more: "Oh, I studied science, I became interested in development, I became interested in gene regulation". That led to more graduate work in actual protein-DNA interactions, the actual molecular interactions. And so I really began to have an interest in that aspect of it, which actually led to my first role in terms of structurally modifying a protein at my first job in industry.
And that, of course, led to, "Gee, there are more questions and more curiosity about how a drug is actually designed or developed". And how do you do that well, and how do you do it correctly, was really a curiosity that drove my varied interests.
Surprisingly — and I think not maybe even easily discernible, at least in the beginning — was that trying to figure out how things are connected leads to advancement because you have cross-fertilization, as a lot of people like to say, and you begin to see where there are inefficiencies between different steps of drug development or process, if you want to call it that in a general sense, that can be linked.
And these improved ways, for lack of any other description, result in, "Oh well, there must be something good about what you're doing". And that results in your career advancement, and that results in you having more opportunity to do the same and have, if you will, more growth to connect the dots and develop what you're interested in. And that really is something that I would say exists at the root of it: you have to be interested in what you're doing. It'll drive a lot of your enjoyment of your work.
David Brühlmann [00:08:08]:
Is that curiosity about how things work, is this also what led you then to leave traditional leadership roles to step into a founder's role? Or how did that come about?
Milan Tomic [00:08:22]:
I think one of the important aspects — I mean, your job is your job, your work is your work. And as much as I can say that you should enjoy your work, you should have fun at your work, which I always strive for, there's also a family aspect, there is also a life aspect. And you have to be able to, at some level, integrate those.
So what could drive your job or choice or even position is really: how does your family integrate with your work and interests? And in my case it's not just family, it's location. Are you comfortable where you are? I mean, disliking the fact — and really hating the fact — that you have to drive in snow for half the year is not going to be helpful for your enjoyment in your career. I mean, not liking humidity or not liking cold or heat — those are important factors. But in any of these, choosing how you move forward is kind of a mix between what you think should be done and how that can be achieved.
My curiosity, my interest in linking things, connecting the dots, is actually what led me to this role. And I explored different aspects during my industry career. And now it came to the point where I could actually look at how some of these aspects can be integrated. And that wasn't as easy for me to find. So I wanted to do that kind of investigation, that kind of curiosity, that kind of furthering my interests, rather than go into larger management roles where my focus would be on other things — managing people or having less science in my role. I hope that answers the question.
David Brühlmann [00:10:26]:
What was the most surprising part as you were stepping into the founder role at Albrem, your own company? What were some unexpected things that happened, and what do you like about this role now?
Milan Tomic [00:10:40]:
Well, the answer would be different for different people. I think what I like about the role is what I just described in the previous question: that it allows for an integration of the kinds of concepts that I feel are important in the drug development area. And that is huge to me.
What I really didn't expect was that it requires a lot of salesmanship. Business I can understand, business I have no problem with. But salesmanship, in my mind, doesn't dive deep into the "how" of things. It is a very different skill that a lot of highly analytical people, scientists, may not have experienced. And so for me that has been a real, if you will, wake-up call that, "Wow, I can't go as deep as I'd want into every situation. I have to focus on getting the business off the ground". And that is a different responsibility driver that caused me to now work more on my salesmanship.
David Brühlmann [00:11:50]:
This is so accurate. Like you, for me running my own company was one of the biggest aha moments. Sales and selling—these are everywhere. It's just all about selling.
Milan Tomic [00:12:01]:
It is, it's pervasive in almost every part of it. And my own experience was really: let the science, the data speak for itself. Others should recognize it. And that's not a part of sales. Sales is really informing in a larger sense. That is not always as easy for someone who wanted to always look at the how. That was a wake-up moment.
David Brühlmann [00:12:29]:
Let's unpack different parts of science. You have worked on various parts of science and various areas of biotechnology. So let's go back to your postdoc work. Tell us a bit—what was the focus and what did you learn during that time about science? You alluded a bit to therapy, translating therapy into what really works, but tell us a bit more. What were specific learnings through your postdoc?
Milan Tomic [00:12:57]:
It is a difficult thing to say there were specific learnings, at least in my case. I mean, I've known people who were very directed and wanted to focus their career path very narrowly, and that wasn't so much my interest. My interest was finding generally how things worked and how they were connected.
And so when I started—and I'll go back a few years before my postdoc—when I started my work as a graduate student, I actually did want to study the interactions of DNA and proteins. And the graduate work, at least studying these things, really did let me into that area. But we got scooped, and three years of my graduate work I couldn't publish. Now, you could take that as a real crisis, but it also led me to kind of piece apart what I was doing and say, okay, well, if I can't do both—if I can't look at, for example, protein-DNA recognition and binding—I could look at DNA by itself.
So I looked at the different structural elements on its own. My graduate work was in nucleic acids and nucleic acid structure, and that led to a controversial thing that my PI was pushing. And that was good—we were able to show that those kinds of things did make a difference in the flexibility of nucleic acids.
But nucleic acids were nucleic acids—there were four repeats. Proteins were much more interesting, much more dynamic. And I got the opportunity to study protein structure in my postdoctoral work. And in a way my postdoc really was looking at that recognition, molecular interaction, in a piecemeal way.
After that, the work in proteins and analytics led to my first industry job, which fortunately for me was also in a structural protein field. What I'm trying to say is: it isn't always "I'm going to go and get this kind of work". It’s more, in my case, that curiosity and desire for deeper understanding allowed me to investigate different aspects that were also of interest to me. So I didn't lock myself into just studying proteins or just studying nucleic acids. It was more, "Let's find out about the how of this or that." They can all be very interesting.
David Brühlmann [00:15:45]:
Yeah, it seems that your curiosity was driving you to go from one thing to another and then yet another thing, and then you had another question which led you to another area of science. And I think this curiosity also led you then to what seems quite a different field, where you were involved in the design and upgrade of a 2,000-liter manufacturing facility. Tell us more about that—what happened then?
Milan Tomic [00:16:16]:
The interesting thing then was really something where you sort of have to look at that point that you brought up, which in my career has really resurfaced multiple times, and that's curiosity—finding out about the "how" of things. And when I moved from Bayer, which was more focused on factor VIII, I moved more into quality control because I was interested in how the quality aspects of the process were figuring into and driving the evolution of a drug or molecule towards being a drug, and wanted to learn more about what is involved in quality.
And prior to that particular project or work you're describing, I had the opportunity to work in QC. I worked on efficiency, product release, raw materials as well as protein product. I had a good understanding of some of the nuances that were involved in the process of drug development. Some of these were good—there were some molecules I really thought were good but failed for legal or liability reasons. Others failed because the design wasn’t efficient or didn’t find a good niche. And that was interesting. At XOMA, for example, there were also bacterial expression systems being worked on at the time that required, honestly, putting resin beads into a bioreactor, which required different impeller speeds and design. So there was a lot of process knowledge and learning while I was in quality.
That led me into quality engineering, validation, and some quality assurance functions as well. But the plant itself wasn’t efficient. And as we were entering that stage, it seemed like there weren’t people positioned to take on that role. So I raised it with management—that this could be improved, that it could be a stronger, more efficient design. And that essentially landed me in the role of rebuilding the plant. Since I had been in quality and had insight into what workflows were most efficient, and knew you needed good throughput, I had an opportunity to apply that, figure out the "how" and use that knowledge to improve the workflows and the facility itself. And that was a very interesting experience. It led to a much deeper understanding of plant design and how a manufacturing facility should actually be structured.
David Brühlmann [00:19:39]:
From this extensive experience, and probably sometimes also challenging experience as you're learning from past failures and then redesigning the factory, and also since you have later worked on bringing products into Phase 1 and Phase 2, what are some pieces of advice you can give to a smart biotech scientist listening? Some are working in smaller companies and need to figure out CMC, others are in bigger companies. But CMC is challenging. From your perspective, what are the most important aspects people should watch out for?
Milan Tomic [00:20:18]:
From a sense of product development—assuming that you've discovered the right molecule—I think the biggest thing to watch out for is the application of that product itself and the actual production of the product. So during development you have choices in how you design a process. Understanding how to make that process robust and how to really make it fit into a particular plant—and by “plant” I mean something that would allow you to produce consistent GMP material—is important. And you need to take that into the design of the molecule and into the design of the development, as much as you need to take into account the actual targeting of the drug itself. So those two working together will give you an idea of a scale process that can be really implemented, and how your drug fits into the whole thing.
That, from a new starting scientist who has a great molecule and a good idea, will give you a good position to be able to talk with your investors, partners, founders, or other collaborators in a way that you can show that it's not just "Oh, I've got something", but more that "I've got something—and it can go ahead, advance, and work to fill a necessary gap." And that will give a lot more traction to your advancement.
David Brühlmann [00:22:08]:
From a Berkeley biochemistry lab to the operational complexity of a 2,000-liter GMP facility, Milan Tomic's trajectory is a masterclass in how scientific depth and operational discipline compound over time. In Part Two, we will shift from biography to practice. We'll look into the value of cell-free protein synthesis and what it actually takes to move a promising protein candidate toward the clinic.
If this episode has been valuable, please leave a review on Apple Podcasts or your preferred platform. And thank you so much for tuning in, and I'll see you next time. For additional bioprocessing tips, visit us at smartbiotechscientist.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let's continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
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Thanks for tuning in to the Smart Biotech Scientist podcast and being part of this journey toward bioprocess mastery. For more insights and practical tips, visit www.smartbiotechscientist.com.
About Milan Tomic
Milan Tomic is a biotechnology executive with more than 30 years of experience in biopharmaceutical development, manufacturing, quality systems, and regulatory compliance. He earned his Ph.D. in Biochemistry from the University of California, Berkeley, following studies in Molecular Biology and Virology. Over his career, he has led antibody product development from DNA through Phase 1 clinical programs, managed GMP manufacturing operations, secured over $150M in U.S. government contract awards, and overseen facility design, quality engineering, and technology transfer initiatives across the biotechnology industry.
Connect with Milan Tomic on LinkedIn.
If you enjoyed this episode you might also like listening to:
Episodes 189 - 190 : Why Smart Biotech Founders Plan CMC First (While Competitors Burn Cash Later)
Episodes 123 - 124: Manufacturability: Why Most Protein Candidates Fail (And How to Pick Winners Early) with Susan Sharfstein
Episodes 213 - 214: From Developability to Formulation: How In Silico Methods Predict Stability Issues Before the Lab with Giuseppe Licari
Episodes 231 - 232: From IND to BLA: The Biologics CMC Decisions That Determine Regulatory Success with Henri Kornmann
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
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What if the biggest advancement in bioprocessing isn’t a new AI algorithm or cutting-edge bioreactor, but a renewed focus on the foundational skills that drive innovation—and a clear-eyed look at what AI can (and can’t) really do for the industry right now?
In this episode of the Smart Biotech Scientist Podcast, Steffen Kreye shares his journey from leadership roles in biopharma to academia, offering pragmatic insights on education, AI, and the future of bioprocessing.
I think teaching and preparing the next generation of bioprocess scientists is extremely important. We need expertise, we need people, we need culture — to bring innovation to market and ultimately help patients. And I think everybody in industry can contribute: talk to universities, support them with expired cell culture media, donate unused bioreactors, or give guest lectures. This is something we can all benefit from. Because good education leads to good people, good companies, good products, and ultimately better therapies for patients.
David Brühlmann [00:00:39]:
Welcome back to our conversation with Steffen Kreye. In part one, he traced a career path from the bench at Glycotope to upstream leadership at Bayer and into the lecture hall at Berliner Hochschule für Technik in Berlin, Germany. He also examined what that vantage point reveals about how we train scientists today. Now we turn to what’s coming: AI-driven bioprocess development, the capability gaps, and the practical advice that matters — whether you are starting out or leading a team. Let's dive in.
Soft skills are becoming more important because AI can leverage a lot of the tedious tasks. What soft skills or emotional skills are you focusing on in education? And what are some gaps you're seeing in students coming into the university?
Steffen Kreye [00:02:47]:
Good question. I think soft skills are getting more important — that's correct. I think teamwork is still very, very important. This is usually not something you learn per se at university. Of course, you have lab courses and you usually do them in groups. For example, we have a GMP project module for GMP manufacturing. Instead of just standing there and reading GMP guidelines, we give the students a project and make it more interactive. They can “play” and develop their own product. We usually just give the boundaries: “This semester you have to produce a bispecific antibody for biopharmaceutical production. Please plan what kind of product you want to produce, plan the process, plan the facility, follow GMP guidelines.” Then they can go on. They can also use AI tools. Teams form, and they have experts for upstream, downstream, facilities, GMP regulations, and the actual science behind the bispecific antibody.
Then you get small teams working together. There are also sometimes conflicts — of course, every time people work together there are conflicts. Some people say, “I don’t want to work with this one,” or “I want to work with that one.” And then we say, well, later in real working life you also can’t always choose your colleagues. You have to work with the people who are there. So I think teamwork is a very, very important skill.
And then I think the most important skill that is really difficult to teach is some kind of self-motivation or drive. People should not expect everything to be prepared for them. They should have curiosity, they should want to understand things, and do things on their own — not just because they have to, but because they want to. I think every employer would agree: I would rather have someone who is very motivated with average results than someone with excellent grades but no motivation, no curiosity, and no drive.
It’s about people loving what they are doing — doing it because they love it, not because they are paid to do it. This is very difficult to teach. You can only spark interest in certain topics. I try to spark interest in biopharmaceutical manufacturing because I think it’s great. Some students resonate with that — they think, “This is awesome, I can use biotech to produce life-saving medicines.” Others say, “This is not for me, I’d rather work in wastewater treatment,” which is also fine. But this is what I can offer — I can share my perspective and enthusiasm. For some students it resonates, for others it doesn’t. And then we have other modules and other professors with different backgrounds.
I think it’s important that students find what motivates them and where they enjoy themselves. I just love working with bioreactors — that is my area where I feel confident and where I have fun. And if I can transfer that excitement to some of my students, I think I have done my job well.
David Brühlmann [00:05:23]:
I would love to have your honest take about AI and machine learning, because there is a huge hype out there. Many people are excited — perhaps there is even an oversell. There is no doubt that it generates buzz, but sometimes there is confusion around what the real value will be, especially in bioprocessing. What do you think about AI in bioprocessing?
Steffen Kreye [00:05:48]:
I recently attended a conference here in Berlin. I think it was also about AI and biopharma. I was really curious to see some use cases and the value you are talking about — where is the actual value for big pharma companies? And at those conferences you maybe see one or two case studies that are interesting and good, but not really breakthrough examples where your jaw drops. Most talks are actually about laying the foundation for AI. So I think what most companies are doing at this stage is basically trying to get their data ready to use AI. And I think this is really complicated. Even before AI this was complicated. I remember from my time at Bayer — and I guess also from your time in a biopharmaceutical company — it's so difficult.
Downstream development uses different tools than upstream and process development. Then you have different sites, and you have GMP and development environments. Even before AI, this was already a real struggle. Everybody was talking about a “data lake” where all data feeds in, but in the end it becomes a “data swamp,” where everything just gets sucked in. And when you need something, you have to go into the swamp and search for your data. So in my opinion, this is where we are right now — building the foundation. And this is extremely difficult, especially in GMP manufacturing, because data readiness and GMP regulations do not always go hand in hand. So I think we are not there yet. Of course AI can help structure data, but I think we need a few more years until companies can really structure their data in a way that allows AI to simply “press a button and do the work.”
David Brühlmann [00:07:20]:
Yeah, that totally resonates. This is something I've seen in various projects, both in big pharma and as a consultant working with companies. And this is key — you need to be able to access your data easily and avoid data silos. Sometimes it's even worse: you have different legacy systems across different sites, and it's very hard to understand what the data means at one site versus another. So there is still a lot of work to be done. Looking into the future, I find this very exciting and I would love your perspective on bioprocessing in general. How do you think we will produce biologics in 10 years from now?
Steffen Kreye [00:08:09]:
What I always preach to my students is the “holy grail” of a bioprocess. In my perspective, I love continuous manufacturing because I find it exciting from a technological perspective — a continuous process with advanced PAT tools, maybe digital twins, and all the exciting developments in the bioprocess community. And then more or less you press a button at the beginning of the cultivation, and the process runs by itself. You don’t need manual sampling anymore. Everything is being monitored with inline probes. You have a model which is running in parallel like a digital twin, seeing how the process runs and also corrects the process. And in the end the continuous process, your final product, just leaves process and you just put in some cell culture media and buffers. So this is something I envision.
This is like the perfect process from a technological perspective. Doesn't have to be the perfect process cost wise or for your specific product or project or company. So this is like the technological dream that's in my mind where I would love to see bioprocessing go to. But of course, in the end, the use case dictates what process is the best. And the cases for standard antibody manufacturing, it's just stainless steel, fat batch, no modeling, easy process and be done with it.
And there will still be even 10 years from now, even 20 years from now, there will still be those use cases where you just need to produce a typical monoclonal antibody and to just stick with the standard process. But the platform processes that we have right now, but with the way the biopharmaceutical landscape is developing, with more difficult to express proteins, other antibody formats, cell and gene therapies, I think the processes are more diversifying and I think all those technologies will to answer at least a few of those problems. That these diversifying processes and products will have in the future.
David Brühlmann [00:09:51]:
What can we as industry leaders do to help you at the university train the next generation of scientists? Or should I say — what should we do? What are a few things we can do right away in our industry jobs?
Steffen Kreye [00:10:09]:
So I think what you could do is just reach out to the university or university of applied sciences in your city and offer to give a lecture. It doesn’t have to be a lecture every Tuesday morning, but if you just stop by for an external lecture and talk about your company, you can also do some informal “advertising,” talk about the challenges, and so on. I think this is very easy to do. We do that a lot here. I wasn’t used to that from my own university background, but here we have many external lectures, especially in the master’s program. Every Friday there are external lecturers who come in and talk about lots of different topics. This is very, very exciting.
So I think this is something everybody can do — a very low-threshold way to get involved. You can simply contact the university and say, “I have this background, I think this is interesting.” You can also contribute to teaching. We have external lecturers from industry backgrounds.
Most of our lab courses are run in blocks over two weeks, and industry lecturers sometimes take a break from their jobs for those two weeks to work here in our labs and bring in a real industry perspective, working with students on bioreactors and practical setups. This is really important.
David Brühlmann [00:11:07]:
I think this is definitely great, and I thank you for the opportunity to do that at your school. Last year was great, and I’m looking forward to this year again. And also, the other thing you mentioned — I did this many years ago when I was at Merck. I was able to teach a module at a university of applied sciences in Switzerland that was part of a biotechnology master’s degree. It’s a great way to give students an industry perspective.
Steffen Kreye [00:11:35]:
I agree. It’s also like the soft skill set you mentioned. Sometimes it’s just as simple as how to set up an online meeting, how to structure an agenda, or how to write protocols and meeting minutes. These are things you usually learn on the job.
And the more you learn at university, the easier it will be to perform well in a job — or even to get the job in the first place. So I think this is extremely valuable for students.
It also motivates them. Of course, they see they have many courses and exams to study for, and they might think, “Why am I doing all this?” But once in a while, when they see someone who is an expert in bioprocessing, modeling, or AI, it really motivates them.
They realize, “This person studied biotechnology 10 years ago — I could be that person in 10 years.” And I think that’s very important. Students should not only learn to get good grades. They should understand that what they are learning is actually meaningful and can lead to doing very interesting work.
David Brühlmann [00:12:34]:
To a student listening to our conversation, Steffen, what single piece of advice would you give them?
Steffen Kreye [00:12:41]:
I would come back to what I said earlier: find what you like — find something you enjoy. And it also helps to find what you don’t enjoy. If you realize during your biotechnology studies that you hate technical work and bioreactors, then do something in genetics, molecular biology, or cell biology — that’s totally fine. Because if you do what you love, everything becomes much easier. So my advice is: look inside yourself, see what motivates you and what you enjoy, and focus on that. If you do something with passion and curiosity, it is much more fulfilling than doing something just because you are paid for it.
David Brühlmann [00:13:16]:
As we’re wrapping up, Steffen, what is one question I should have asked in this interview?
Steffen Kreye [00:13:22]:
I think we covered the education part quite a bit. Maybe one question is whether I ever regret going into academia. Actually, I never did. It was a big change going from industry to academia. When I started here, I was used to aligning everything with multiple teams — in industry you have four or five meetings a day just to synchronize timelines and updates. When I started here, I thought I had to schedule meetings and invite everyone. And people would ask, “Why are you asking us? You can just decide yourself.”
What I really like about my job is this freedom. I am responsible for my modules, lectures, and lab courses, and I can design them as I want. Sometimes I experiment — I try integrating a new lab experiment. Maybe it works, maybe it doesn’t. If students find it useful, great; if not, I remove it next year. I really enjoy this freedom.
I also miss working with modern bioreactors and cutting-edge equipment. At the moment we are in a good situation, but in 10–15 years our equipment will also age, and we will need new investments. Another thing I miss is working in large teams. Whenever I share a course or module with a colleague, it always becomes better because you get two perspectives instead of one.
Overall, I never regretted going into academia. It is very motivating to see how students develop. I see them in their early semesters, then later in lectures, lab courses, bachelor’s theses, and master’s theses — and eventually they become professionals in industry. And now, after five years, I see the first cohort I taught graduating with their master’s degrees. It is very rewarding to know I had a small role in their development. Sometimes they tell me, “I finished my master’s thesis and already received a job offer before submitting it.” That is very satisfying to hear.
David Brühlmann [00:15:45]:
This has been great. Steffen, what is the most important takeaway from our conversation?
Steffen Kreye [00:15:53]:
I think teaching and preparing the next generation of bioprocess scientists is extremely important. We need expertise, we need people, we need culture — to bring innovation to market and ultimately help patients. And I think everybody in industry can contribute: talk to universities, support them with expired cell culture media, donate unused bioreactors, or give guest lectures. This is something we can all benefit from. Because good education leads to good people, good companies, good products, and ultimately better therapies for patients.
David Brühlmann [00:16:32]:
Where can people get a hold of you? If they want to donate a bioreactor, provide media, or give a lecture — where can they reach you, Steffen?
Steffen Kreye [00:16:41]:
The easiest way is LinkedIn, of course. You can find me there under my name. Just reach out and send a message — that’s the simplest way. You can also find my contact details on the university website of Berliner Hochschule für Technik. Either email or LinkedIn works fine.
David Brühlmann [00:16:54]:
Well, thank you so much for being on the show today, Steffen, and for sharing this unique perspective on education and where it is going. Thank you very much.
Steffen Kreye [00:17:04]:
Thanks. It was a pleasure. It was really good talking to you, David.
David Brühlmann [00:17:09]:
The scientists who will define this industry in 2035 are sitting in classrooms right now or early in their industry roles. What they learn — and how they learn to think — will shape therapies that do not yet exist. That responsibility belongs to all of us. If today's conversation added value, please leave a review on Apple Podcasts or your platform of choice. Thank you for being part of this community.
For additional bioprocessing tips, visit us at smartbiotechscientist.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let's continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
If you found value in today’s episode, take a moment to like, follow, and leave a review on Apple Podcasts or your favorite platform—it helps us reach and support more scientists like you.
Thanks for tuning in to the Smart Biotech Scientist podcast and being part of this journey toward bioprocess mastery. For more insights and practical tips, visit www.smartbiotechscientist.com.
About Steffen Kreye
Steffen Kreye is an industrial biotechnology expert and professor at the Berliner Hochschule für Technik. His career spans more than a decade in bioprocess development, starting with a PhD at TU Berlin on mammalian cell line engineering for monoclonal antibody production. He progressed through multiple roles at Glycotope—from scientist to director of cell line and upstream process development—before moving to Bayer Pharmaceuticals as a lab head in upstream development. Since 2021, he has been back in academia, focusing on teaching and applied research in industrial biotechnology and bioprocess engineering.
Connect with Steffen Kreye on LinkedIn.
If you’re interested in exploring further the concepts we touched on, take a look at these related discussions:
Ep 175–176 : How Virtual Reality Training Solves Europe's Bioproduction Talent Shortage with Sandrine Lemoine — about training the next generation of biopharma talent.
Ep 93–94: From Lab Coat to LinkedIn: Benjamin McLeod's Journey to Cell and Gene Therapy Influencer — another career pivot story from a scientist who stepped outside the traditional industry path.
Ep 111–112: AI Meets Biology: Why Domain Expertise Still Rules in the Age of Large Language Models with Lars Brandén — very aligned with Steffen's nuanced take that AI is a tool but human expertise in bioprocessing still matters.
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
How do you bridge the gap between cutting-edge industrial bioprocessing and the academic world tasked with training the next generation?
This question sits at the heart of a recent episode of the Smart Biotech Scientist Podcast, where host David Brühlmann interviewed Steffen Kreye, a bioprocessing expert who transitioned from leading upstream process development at Bayer to shaping industrial biotechnology curricula at the Berliner Hochschule für Technik in Berlin.
I think we have a much stronger focus on actual lab work. So when I talk to people who hire our students for bachelor’s theses or master’s theses, or even later on for jobs, they really say: you can take our students and put them in a lab and they start working immediately.
A university student might go back to their office and read papers for two weeks before doing their first experiments, which is also a good way to approach things. But our students really have this hands-on experience and this hands-on mentality. So they go into the lab and they want to work with their hands, they want to do stuff in the lab, and they really enjoy the lab work.
David Brühlmann [00:00:37]:
What does it take to walk away from a senior industry role at Bayer running upstream development and choose the classroom instead? Today's guest, Steffen Kreye, did exactly that. With a career spanning Glycotope cell line development labs to leading upstream development at one of the world's largest pharmaceutical companies, he has now become a professor for industrial biotechnology. He trains the next generation of bioprocess engineers at Berliner Hochschule für Technik in Berlin, Germany.
In part one, we explore his journey, what drove the pivot and the structural challenges facing applied science education today. Welcome, Steffen. It's great to have you on today.
Steffen Kreye [00:02:37]:
Yeah, thanks for having me.
David Brühlmann [00:02:38]:
It's a pleasure. And it's great to reconnect. To get us started, Steffen, share something that you believe about bioprocess development that most people disagree with.
Steffen Kreye [00:02:50]:
That's a tough one from the start. But I think in one of my lectures I always put: “The process is the product.” A very famous quote, I guess, in bioprocessing. And it's kind of true, but your process can be super nice, super sophisticated. But if the product itself — speaking of biopharmaceuticals — isn't working in the clinic, then the process is more or less worthless. So I think, of course, the process is important and we as process engineers really think that it defines the product. And it does. But as I said, it's an important phrase. However, if the product is not working in the clinic or in human trials, then the process can be as nice, as continuous, as AI-driven, or whatever. You don't have a use case for the process. That's something I would not, let's say, 100% agree with.
David Brühlmann [00:03:34]:
A good piece of advice is to always start with the end in mind. And this is what comes to my mind when you're sharing this. Steffen, you have an exciting career path. Draw us into your story. Tell us what sparked your passion for biotech and what were some interesting pit stops along your way.
Steffen Kreye [00:03:54]:
Where do I start? So I guess in school, I wasn't usually the best student, I would say, but in senior high school my interest in mathematics, chemistry, and biology really sparked and, for some reason, I got good at it. I don't really know why. Something clicked in my mind and then I wanted to study this area. And I think biotechnology is the perfect example of combining mathematics, technical aspects, chemistry, and biology. So this was a good fit.
My brother also thought about studying biotechnology. He ended up studying chemistry, which he also really enjoyed. And so I applied for a biotechnology program. And I see the same with my students now. It's very interesting when people start studying biotechnology. I would say most of them don't really know what biotechnology is really about.
I still remember my physics lecture. Physics was my worst module in my whole study course. But there was a PhD student who gave some advice and some lecturing, and he was like, “Well, you guys are biotechnologists and you will be standing in front of a big tank and you will be culturing cells.” And I was sitting there in the first semester, and I was like, “That's not really what I want to do. I want to work with biochemistry and cells and genetics and stuff like that.” And now looking back on my industrial career, I was always standing in front of a big vessel and I was cultivating cells. So it came full circle.
So when I studied biotechnology, I already thought the whole package was really interesting. I got interested in biochemistry. The technical aspects really came later on and led to a focus on bioprocess engineering. Some of my fellow students focused more on cell biology or microbiology. But I always found the technical aspect really interesting.
And I think my study abroad during my master’s in Canada was really interesting and was also the first time I had the pleasure to work with CHO cells. So I think this was an important step. I didn't realize it at that time, but this basically laid the groundwork for my PhD, which I did externally in a company called Glycotope, which is not around anymore, but the affiliated company FyoniBio is still around.
And I think the master’s thesis and then the aspect of bioprocess engineering with mammalian cell production of biopharmaceuticals — this is really where I got good at it. And then, of course, the PhD thesis is also a journey in most cases. I realized during this journey that the initial idea of my PhD thesis was to connect cell line development and molecular biology with the process. But I ended up doing mostly process science and process engineering. And I really enjoyed this — working with a big bioreactor instead of pipetting small amounts of liquid from one tube to another. So this is really where I got into bioprocess engineering: working with bioreactors and also working with biopharmaceutical proteins. I think this was also a very important step in my career.
And then it more or less went along. So I finished my PhD and then I was lucky to stay within the company where I did my PhD. I worked there as a scientist, later on as a group leader for the upstream development team. So this was also very exciting — the first time managing people. And yeah, that was a nice development, I think.
But coming from this rather smaller company, I always had in mind: how do big companies do what I'm doing right now? One time I was looking at job offerings and there was a posting from Bayer. They were looking for a process expert in upstream development. I more or less checked all the boxes and then I applied, got an invitation, had a nice interview, and then I started at Bayer working as an upstream process expert, which sounds nice and fancy.
And later on I got promoted to lab head. So again, I had a small team. And then I worked with Bayer’s project pipeline, developing cell culture processes and also scaling them up and transferring them to the clinical manufacturing sites. This was an interesting journey. When I tell this to my students, usually one of them raises their hand and asks, “Why are you here now? What made you change and come to university or academia?” And this is again a good question.
David Brühlmann [00:07:44]:
That's an excellent question. Yes.
Steffen Kreye [00:07:47]:
It wasn't always like this dream of mine. So a friend of mine, who I studied with and who also became a professor, always had this dream that he would become a professor. I never really had this dream. It was kind of strange. I was enjoying my time at Bayer. I had great colleagues. The stuff that I was doing was awesome. I was doing the stuff that I always enjoyed — working with bioreactors and so on. But I realized that a big corporation is not really my thing. Things kind of move slowly, there’s a lot of bureaucracy. So this is not really something I wanted to do until retirement.
One Sunday afternoon I was sitting on the couch and for some reason I was googling “professor biotechnology.” And in Germany here we have two different kinds of universities. We have the, I would say, traditional universities and then universities of applied sciences, which are more comparable to university colleges. You still get a master’s and a bachelor’s degree, more or less fully comparable to a university degree, but the education is more focused on practical and industrial application.
And I knew, because I had been in industry for seven or eight years, that the classical academic pathway would be more or less impossible or at least very difficult. I didn't have a lot of publications. I had a few patents from my time in industry, but I didn't have the track record of publishing five or six papers a year. So a classical academic career at a traditional university was difficult.
But with my profile, I thought this was a really good match for a university of applied sciences. So I was looking explicitly for this kind of job. And then I googled it one Sunday afternoon on the couch, and the first hit on Google was the job that I'm having right now. So it was something aligned by the universe that this job offering was there at the exact moment when I felt like googling it. And I more or less checked all the boxes again. They were looking for someone who had bioprocess experience working with cell culture. Design of Experiments appeared in the description — it looked like a perfect fit. And then I applied.
The application process for professorships can be quite long and tedious. Also, during this period, COVID-19 happened and this made the application process even longer than I expected. But I guess from sending out the application to having it on paper that I would become a professor was around a year, which is more or less normal even without COVID. So it's a long and very formal application process.
And since five years now, I'm a professor for industrial biotechnology here in Berlin at Berliner Hochschule für Technik, the University of Applied Sciences. And my focus is industrial biotechnology. It's a very broad field, but my focus area is, I think it’s called, applied cell culture. So it really fits well with my profile.
David Brühlmann [00:10:28]:
Yeah, that's exciting. It seems also like a natural evolution of what you've done before. And then it looks like you got interested in the teaching part, but now you can leverage a lot of your expertise and knowledge that you acquired both in a smaller company and in big pharma.
For those who are not very familiar with the, shall I say, dual education system that's very prominent in Germany, also in Switzerland, and in other parts of the world — perhaps people are not so familiar with that. So the mission of universities of applied sciences is to prepare the students directly for industry. Can you tell us a bit what that means? How is, for instance, the teaching or the curriculum different from a traditional university?
Steffen Kreye [00:11:16]:
Right. I think we have a much stronger focus on actual lab work. So when I talk to people who hire our students for bachelor’s theses or master’s theses, or even later on for jobs, they really say: you can take our students and put them in a lab and they start working immediately. A university student might go back to their office and read papers for two weeks before doing their first experiments, which is also a good way to approach things. But our students really have this hands-on experience and this hands-on mentality. So they go into the lab and they want to work with their hands, they want to do stuff in the lab, and they really enjoy the lab work.
And this is also reflected in the curriculum. So we have a strong focus on lab courses. I did my bachelor’s and master’s at a university. Of course, I also had practical training, but not as much as here right now. So, for example, in the fourth and fifth semesters here in our bachelor’s program, the students only have one lecture-based module. Apart from that, they have lab course after lab course after lab course: cell biology, microbiology, genetics. And they also have the option of choosing different elective courses depending on where they want to specialize.
So they really get this hands-on experience, which I think is very valuable because they still get the scientific background, but they also really know how to work in a laboratory. I think this is the key part. But this also brings quite a long list of issues because running a lab and teaching in a lab is very expensive. So if you do computer science or philosophy, you more or less need a computer and maybe a library or something. That's it. But if you run lab courses — especially in a broad field like biotechnology — you need bioreactors, laminar flow hoods for cell culture, microbiology and biochemistry equipment, mass spectrometers, and so on. This is all very expensive.
And this is something that's difficult at the moment because universities in general, I believe, are becoming more financially focused in how they evaluate education. But we of course want to keep our lab focus because this is what differentiates us from traditional universities. And if we are told now that lab courses are too expensive and that we should do more lectures instead, then we become more similar to universities and lose our unique selling point.
So this is really difficult at the moment. I think that's something a lot of universities of applied sciences are struggling with because lab work is just expensive — especially in my field because I come from the biopharma industry. And if I show students a slide with a bioreactor system from a well-known company and tell them this is used for process development and costs half a million euros — which I guess is not that much money for a biopharmaceutical company — for a university that's a huge investment. You’re happy if you get that kind of investment once in your career. And then, of course, you have the ongoing costs of cell culture media, consumables, and so on. So this is really, really a challenge.
David Brühlmann [00:14:02]:
How do you partner with companies? Perhaps there are some investors or other ways to overcome these challenges to stay relevant. And I agree with you that the lab work, the practical work, is extremely important because you don't learn how to operate a bioreactor just by looking at slides. So it's absolutely key. What are some strategies you are undertaking now to still be relevant and to still be able to offer these expensive lab courses?
Steffen Kreye [00:14:34]:
One aspect — which is not really a strategy, but more or less coincidence — is that we are currently in a brand-new building and were lucky to receive some investment connected to this move. So we were able to get new bioreactors, which are very nice, and additional equipment. So at the moment we are quite happy with our equipment situation. But of course the ongoing costs are still important, so we have to see how we handle that.And you mentioned one solution already: partnering with industry. And this is of course something that we do. I have a good network in the Berlin area. Berlin is maybe not that well known for the big players, except for Bayer, but there are a lot of small and mid-sized biotech companies. And with most of them we have good relationships. There are some companies doing basically what I was doing before — process development, upstream process development, downstream process development. Some of our students actually ended up joining those companies and are having a good time there.
And whenever there’s cell culture media that is close to its expiration date or even already expired, I get a call saying there’s some media left over. They can’t use it anymore for GMP work, but for teaching it’s still perfectly fine. If the cells grow 10%, 20%, or even 30% less than their maximum growth rate or viable cell density, that’s still okay for educational purposes. And then I get those materials as donations from the companies.
The same happens with other consumables — Protein A resins, for example. If they’ve already been reused many times in industry, we can still use them for teaching. The same applies to older equipment. Companies contact us, and if you have the right network, you can really leverage these relationships to save money and also stay relevant. The students also get exposed to the same materials and systems used in industry. And for the companies, it’s also a form of branding because students see the company names on media bottles, flasks, or equipment. So both sides benefit. This works really nicely.
The other aspect is that, due to the nature of a university of applied sciences, my colleagues and I usually don’t run large independent research groups. At a traditional university you often have professors with several postdocs, PhD students, master’s students, and bachelor’s students. Here, only a few colleagues have that kind of structure. Most of us are really focused on teaching and training students.
As a result, bachelor’s and master’s theses are usually completed outside the university. Students go to companies around Berlin and Brandenburg, sometimes elsewhere in Germany or even abroad. And this is also very interesting for me because I supervise and evaluate theses that were not done in my own lab. So students go to Bayer, other biotech companies around Berlin, or to Charité — where I think most of our students end up. Charité is one of the biggest university hospitals in Germany, if not Europe, and they do cutting-edge research there. And then you read a bachelor’s thesis or a master’s thesis and it becomes a way for me to stay up to date with recent developments in the field.
David Brühlmann [00:17:17]:
Speaking of staying up to date, there is no doubt about that. We're in a very fast season of change in the industry. There's so many new technologies coming — modeling, digital technologies, AI, and lots more robotics. How do you stay relevant in your teaching and also in the context of the challenges you've just mentioned regarding infrastructure? How does that work?
Steffen Kreye [00:17:43]:
Similar to what I've mentioned before, one way to stay relevant and up to date is through the master’s and bachelor’s theses that I read. Very exciting. It's also always educational for me to read something new. Apart from that, of course, visiting conferences is nice, which is also usually connected with fees, which is of course normal. But coming from academia, you usually get a discount. So conferences are always useful for me to see what's going on, which topics are relevant, and so on.
I think this is really important. I don't have the perfect answer right now, but I just realized this semester — I've been teaching for five years now — and most of the slides were prepared five years ago. When I talk about biopharmaceutical development, how many biosimilars are out there, what the capabilities of CMOs are, which CMOs are most relevant, or what global cell culture capacity looks like, I notice that I collected all these numbers five years ago and they are still on my slides.
Sometimes I see projections from 2021, and the projections go to 2022, 2023, 2024 — and I just realized those were projections five years ago. Now the world has moved on. Even those topics that I mention are not really fast-evolving compared to areas like AI. Cell culture capacity, for example, is not changing as rapidly as digital technologies. So I never wanted to become one of those professors where you open a lecture and at the bottom of the slide it says “Summer Semester 1988” or something like that. So I really don't want to do that.
After five years I realized I need to go over my slides again and update the numbers and include more recent publications. This is an ongoing process. I would love to have more time to do this. As a lecturer at a university of applied sciences, you typically have to do twice as much teaching as at a traditional university. And this takes up most of my time. In the morning I'm giving lectures, and in the afternoon I'm in the lab with the students, which is also very unique for our university. When I studied at a traditional university, in five years I was maybe in the lab once with a professor. In most cases, lab courses were taught by PhD students who had to do it — they weren't necessarily excited about it. Here, we actually want to do it. So our engagement and excitement is much more visible.
This takes up most of my time. What is really fun, though, is taking slides from five years ago and seeing what has changed — and updating them. Maybe including more relevant topics like cell and gene therapy. This is something I always emphasize: if I were a student today, I would probably go into that area because it's extremely exciting — with risks, but also huge potential impact. As a process engineer, especially, the processes for cell and gene therapy are developing as we speak. I don't have that much experience in this area yet — my background is mainly in classical biologics, proteins, antibodies, and so on — but I think this field is very exciting. I would love to work in it.
David Brühlmann [00:20:33]:
Yeah, it's definitely exciting. I mean, there's so much going on. And as you mentioned, in cell and gene therapy, for instance, I'm wondering, Steffen, how has the teaching style or teaching strategy evolved, especially with AI or research itself? We now get access — what used to take weeks to gather from publications can now be done with an AI prompt in minutes, giving you a huge list of recent papers. So that changes the game quite a lot. What is your perspective? What have you already implemented, or what is coming, in order to provide the best possible teaching style and content for students and to prepare them for today's industry?
Steffen Kreye [00:21:18]:
That's an excellent question. I think maybe we can break it into two parts. What I realized when I went to university is that some professors come into the lecture hall, present their slides, and when the time is up, they leave. For us here at the University of Applied Sciences, we've tried to engage much more with students. It's not just standing in front and speaking for 90 minutes. It's more like a dialogue — more like a seminar.
We use polls, small interactive elements, or we give students challenges and let them work on them in small groups for 5–10 minutes and then reconvene. I think that's changing, and it's also more relevant today than simply memorizing facts — really thinking about problems and how to solve them. This shift was already happening even before AI.
And AI is, of course, a big topic in education. I think the classical semester project is not really worth it anymore. If you have a theoretical project and write about it, you can give the right prompts to an AI and get a very good-quality report in our domain.
However, lab work still cannot be replaced by AI. And even creating graphs and interpreting results is still difficult for AI to do properly. Writing an introduction, for example, is perfect for AI — no problem at all.
So sometimes when I request lab protocols, I even tell students they don't need to spend time writing the introduction because AI can do that in one minute. They should focus instead on structuring their data and interpreting their results. And of course, literature research is another big area. I still remember my PhD, bachelor’s, and master’s work — searching for papers was very time-consuming. AI is a huge help here, and I want students to use it because it's a tool that is not going away. They need to learn how to use it correctly.
But if they write an introduction and it is incorrect or imprecise because of poor prompting, they cannot say “that was the AI.” It's still their responsibility. And when they use AI, they must cite it. We have guidelines stating how to write scientific reports, and they must include a section explaining which tools they used and how they used them. From a grading perspective, I still evaluate things like language quality and grammar. But I think this is becoming less relevant now because AI can polish any text into a formally perfect document.
So the focus is shifting toward understanding: did the student actually understand what they did and what the results mean? We still have oral defenses for bachelor’s and master’s theses, and I think this will become even more important. Instead of only evaluating written work, we need to verify understanding in discussion — whether the student really did the work or whether AI produced it.
David Brühlmann [00:24:15]:
That tension between industry pace and academic cycles is something every educator in biotechnology is navigating right now. And there are no easy answers. In part two, we go deeper with Steffen Kreye. We look into AI, machine learning, and bioprocessing, which human skills are becoming more valuable, and what a well-trained bioprocess engineer will need to look like in 2035.
If this episode resonated, please leave a review on Apple Podcasts or your preferred platform. It means a great deal to me. Thank you for tuning in. 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.
If you found value in today’s episode, take a moment to like, follow, and leave a review on Apple Podcasts or your favorite platform—it helps us reach and support more scientists like you.
Thanks for tuning in to the Smart Biotech Scientist podcast and being part of this journey toward bioprocess mastery. For more insights and practical tips, visit www.smartbiotechscientist.com.
About Steffen Kreye
Steffen Kreye is Professor of Industrial Biotechnology at the Berliner Hochschule für Technik in Berlin. He combines deep academic expertise with extensive industry experience, having worked his way from PhD research in bioprocess engineering at the TU Berlin into leadership roles in upstream process development. Before entering academia, he held senior positions at Glycotope and Bayer Pharmaceuticals, where he led cell line and upstream development teams focused on recombinant protein and antibody production. Today, he trains the next generation of bioprocess engineers at the intersection of science and industry.
Connect with Steffen Kreye on LinkedIn.
If you’re interested in exploring further the concepts we touched on, take a look at these related discussions:
Ep 175–176 : How Virtual Reality Training Solves Europe's Bioproduction Talent Shortage with Sandrine Lemoine — about training the next generation of biopharma talent.
Ep 93–94: From Lab Coat to LinkedIn: Benjamin McLeod's Journey to Cell and Gene Therapy Influencer — another career pivot story from a scientist who stepped outside the traditional industry path.
Ep 111–112: AI Meets Biology: Why Domain Expertise Still Rules in the Age of Large Language Models with Lars Brandén — very aligned with Steffen's nuanced take that AI is a tool but human expertise in bioprocessing still matters.
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
Can aging be fundamentally slowed or even reversed—not by science fiction, but by harnessing the unassuming power of super-early stem cells?
For years, the conversation around extending healthspan has been filled with dead-ends and hype. Yet, beneath the surface of the stem cell world lies a unique source: pre-placental tissue obtained only from non-viable ectopic pregnancies.
In this episode of The Smart Scientist Podcast, David Brühlmann talks with Yuta Lee, CEO of Accelerated Bio about the groundbreaking advances in stem cell technology and their potential to revolutionize healthspan and longevity.
That means now you can actually rejuvenate an old mouse purely by function of proteins coming off the blood of the young mouse. And his idea was, well, if you can do that, I wonder what happens if you take the earliest stem cells and all of the secretome or proteins coming off of it and put it towards senescent cells. Senescent cells are these cells in our body that stop dividing, but they're sitting around and refuse to die and they're putting out a lot of inflammatory messages that are now connected to a lot of aging diseases we have today. And so we went through that whole study in vitro and in vivo and we discovered that using these super early secretome, we're able to systemically downregulate all the inflammation.
David Brühlmann [00:00:46]:
Welcome back. In part one, Yuta Lee, CEO of Accelerated Bio, walked us through the foundational biology of human trophoblast style stem cells. What makes them distinctive, how they are sourced, and what their manufacturing profile enables. Now we turn to the larger ambition, aging itself. What does the science of biological decline actually tell us? And where does a stem cell platform fit into a vision that goes beyond treating disease to preventing it altogether? Let's find out.
I just like to backtrack a little bit because I mean cover quite a lot of different concepts and I just want to make sure that everybody understands. So you alluded to it during the introduction that we are getting the cells from an ectopic pregnancy. Tell us a bit more just what happens there. Why is this ethical source? Because it's still coming from a pregnancy. I want smart Biotech scientists listening to understand why this is an ethical sourcing.
Yuta Lee [00:03:02]:
That's a really, really great question. And we get asked that by everybody. Okay, so what happens is this just to repeat some of the basic stuff though. The embryo gets stuck in a fallopian tube and it'll grow until it bursts the tube. So, so if you don't perform the salpingectomy, which is a surgery to remove that mass, then the mother may die of internal bleeding. And so what happens is we get a call from the hospital that we're working with and they say, hey, a woman came in with an ectopic pregnancy. We went through the consenting process already. They signed all the forms, you can come and get it. And so we go and we wait outside the operating room and we wait for them to take it out. And so what happens is in the salpingectomy, they ablate or they burn both sides of the tube and remove the mass.
Once they remove the mass, all they have to do is scrape off the pre placental tissue and hand the rest of the mass back to a secondary, usually a pathologist who will double check the epidemic topic and throw it away. They hand us the pre placental tissue called the chorionic villi. We put it in an ice box and we take it back to our lab for processing. How we pass all of the ethical reasons are three main key points.
One, you absolutely have to perform the salpingectomy to save the mother, because if it bursts internal bleeding, the mother may die.
Two, the fetus is already non viable. So a normal embryo needs to implant by day seven, day eight, but we don't discover until four to eight weeks. So all obgyns understand the fetus to be non viable. And so all they have to do when they take it out is give it to pathologists to check and they can throw it away.
And the three, we only take the pre placental tissue. We don't even touch the rest of the embryo, the fetal part. So if you add all those threes together, we have the perfect clearance for ethics. No problems at all.
David Brühlmann [00:04:46]:
Fantastic. Yeah, this was simple and clear. And it's great that we have now such a new type of stem cells at our disposal. That solves the whole accessibility or manufacturability issue. Besides the sheer numbers, are there some other differentiation power capabilities that much earlier cells have versus MSCs or IPSCs?
Yuta Lee [00:05:09]:
Yes, they do. So I always go back to fetal development. Remember, the first eight weeks is embryonic, and then during a fetal period, what happens is the inner cell mass, which is the lump in the middle that turns into a baby, starts to divide in various types. Ectoderm, mesoderm, and endoderm. Three different layers. And each of those layers will turn into different types of cells in our body, ecto being the external part, our skin, our brain, our eyes. The endoderm is everything from our mouth to our anus, everything connected to the gut tube as endoderm. And then you have mesoderm, everything that fills in between the bones, the blood, the muscles. And so by that fetal period, all the cells dividing, differentiating. And so what you want to think about is if you're getting cells from a much later stage, that means these cells have been quite fully differentiated. But if you're getting cells from a very, very early stage, think about all of that information that's about to explode and turn into a baby. But it's still encapsulated, so it's going to be very biologically active.
Also, the differences between the cells really are all about methylation. So if you know about the epigenetic layer, so at the epigenome is the turning of the genes on and off to kind of define what that cells will turn into. And so at the early stage, they're going to be very hypomethylated. So there's not much information on it. So it's very, very primitive, very early on, so there's not much information. So as an example, if you were to take an IPSC and differentiate it into a neuron, it might take upwards of 16 days, maybe a couple of weeks or more to take out IPSC and turn it into a neuron. Our cells takes one day and one protocol step turns into a neural progenitor that is th positive. That means you tested positive for dopamine. We also can do the same thing with pancreatic progenitor cells also. One day and one protocol step. And when you put sugar on it, insulin will come out very unusual. And so that's our evidence that the earlier cells are more easily programmable. Pretty cool.
David Brühlmann [00:07:19]:
So what are now the therapeutic areas that you have in mind with your novel platform?
Yuta Lee [00:07:27]:
It was actually not my idea. It was actually the National Institute on Aging or part of the NIH. So I'm going to tell you the story, if you don't mind. So about three years ago, a researcher from the National Institute on Aging, which is part of the NIH in Baltimore, calls me up and says, Yutta, we saw one of your presentations in Seattle, and we hear that you have the earliest stem cells without any ethical issues. I said that we do. And in fact we've just done an engineering run headed towards GMP. And he says, can you send me all of your conditioned media, your spent media? So we packaged up 6 liters of spent media, we sent it to Baltimore. Then I call them up and I'm like, hey, what are you doing with this stuff? And he proceeds to tell me one of the craziest stories I've ever heard. He says, have you ever heard of the heteroconic parabiosis studies done at Stanford about 20 years ago? I said no. He goes, okay, these two researchers at Stanford took two mice and stitched them together like Siamese twins so they would have one circuit blood system, one old mouse and one young mouse. And it turns out that the old mouse is biologically getting younger and the young mouse is biologically getting older. They're like, whoa. Second experiment, they did not stitch them together but did a plasma transfer and the same thing was happening.
So what does that mean? That means now you can actually rejuvenate an old mouse purely by function of proteins coming off the blood of the young mouse. And his idea was, well, if you can do that, I wonder what happens if you take the earliest stem cells and all of the secretome or proteins coming off of it and put it towards senescent cells. Senescent cells are these cells in our body that stop dividing, but they're sitting around, refuse to die, and they're putting out a lot of inflammatory messages that are now connected to a lot of aging diseases we have today. And so we went through that whole study in vitro and in vivo and we discovered that using these super early secretome, we're able to systemically downregulate all the inflammation. So this is incredible. And so what I'm doing now is taking this material, we're going to manufacture GMP and provide it as a source material for people to go after any indication based on inflammation. So that hits on a lot of neuro diseases, it hits on a lot of autoimmune diseases. Anything that has to do with inflammation we could probably solve systemically, which is pretty cool.
David Brühlmann [00:09:50]:
How would that therapy work? Would that be a one time treatment and then it's solved
or would that be a long or several year treatment
Yuta Lee [00:09:59]:
That is to be discovered by the PIs, but we need to figure out the dosing and the frequency to do that and it takes human clinical trials to get that done. We're very, very excited about what we can do. And if you don't mind. I'm just gonna do a natal extension of that.
Unfortunately, the regulatory bodies around the world are designed around sick care. So if you don't get sick, basically no one pays to help you. I really do believe in prevention. And so in prevention, what I actually think can happen is imagine that we're sending the earliest messages from these early cel into your body that can now communicate with your existing cells to be young again, be regenerative again. And let's assume that we can either slow down, stop or reverse biological aging in your body. So this is different than your chronological age, like 54 this year, next year I'll be 55. Nothing to stop that.
But if you were to take biopsies of every organ in my body, it'll come in at a different age, at a standard deviation off of your chronological age. So we actually believe that from the parabiosis studies, the two mice being stitched together, that you can actively reverse that. So if you can do that systemically, that means imagine you were 25 and you start injecting this material as a prevention. When you're a hundred years old, you still have internal organs of a 25 year old. I believe that's the proper way to push ourselves beyond 150. That's very exciting to me.
David Brühlmann [00:11:20]:
Wow, that's exciting. What I like is, well, I'm totally on your page. We should focus a lot more on prevention. And it reminds me when I read Peter Attia's book and he said, well, we are waiting way too long until everything falls off the cliff, so we should start way earlier, whether it's cancer, whether it's diabetes, obesity and all these diseases. So I'm just thinking about this blood and the mice story and the potential of the cells. So would that mean in the near future that we would have some kind of product our disposal? We can take that as a one time dose or several dose or whatever at a regular rhythm and we would get younger or we would at least maintain our age. So how far away actually are we from that?
Yuta Lee [00:12:05]:
I think very, very soon. That's a jurisdictional issue. Meaning in the US and in Europe it's going to be a lot more strict, but in other places around the world it's probably a little more relaxed. And so we're a clinical company, so we actually want to properly do it and get it through human clinical trials and bring this product officially to the world. And so I actually think that within five years we can have this product properly made for people at the scale where everybody can afford it. So in some of my keynotes that I go out and do sometimes, what I like to say is, with our scale of the cells and how much it can grow, we're the only platform that I believe we can safely deliver this material as a prevention for everybody to use, no matter what kind of socioeconomic condition you are. And at that scale, you can drive the cost down almost nothing. And so how do we stay healthier longer and start pushing on longevity? How we can live longer? That's exciting to me.
David Brühlmann [00:12:59]:
That's definitely very exciting. And what do you think are the limits of this technology? Because you mentioned the senescent cells, they become more frequent, especially as people are aging or made some poor lifestyle choices. Could you even reverse the damage that was created, or would there still be a limit?
Yuta Lee [00:13:20]:
Somehow I actually think we can reverse a lot of damage. But what you said earlier was actually quite true, is that once your body is dysregulated, so you fall off of homeostasis, it's actually really hard to bring it back. And I have this conversation with Brian Kennedy all the time, which is one of the topical longevity scientists or aging gerontologists. So what we want to do is prevent ourselves from falling off that cliff in the first place. But I believe that whatever damage we have already done, if you start early enough, you could probably reverse it or fix it, because our bodies are actually quite resilient. We just need to give it a chance. And if you start early enough, you can probably avoid a lot of damage in your later years.
David Brühlmann [00:14:01]:
We've already talked about the future, the near future, but I'd love just to open it a bit more to get your thoughts on how you envision the future of stem cells in general. And if I may add, stem cells and cell therapy in general. What are the new technologies that are coming?
Yuta Lee [00:14:20]:
I am such a big proponent of cell and gene therapy. So there are two sides. It is you can use the cells, you can genetically fix things with gene therapy. That, to me, I think, is the most exciting field going forward in healthcare and in medicine, I think gene therapies, everybody knows, go in and fix the gene that's wrong, and you can put your body back to being normal. Cell therapies are very exciting, too. You can do replacement. You can reprogramming. Believe it or not, there are already 43 - last count that I did - cell and gene therapies already approved by the FDA. Most people don't know that.
And more and more coming literally every year for the last 200 years we've been living through an era of chemical drugs. We designed drugs to put it in our body to literally block certain protein cascades or processes so that we won't suffer the symptoms anymore. And that's been largely very, very effective. But when you use chemicals, obviously you take the hits with your kidney and your liver, because these things your body doesn't know how to deal with. So maybe that's correlated to increased liver and kidney disease. But, you know, in the new era in cell and gene therapy, I believe that we are now addressing the core problem at its root, cells are going wrong. Let's send some new ones in there to fix it. From the very beginning, if your genes are wrong from mutation and whatnot, let's go and edit it out.
These are things that we are actively seeing almost on a weekly basis, success at different clinical stages, which is, I think, going to create a lot of miracles in the next five, 10 years. And I believe that through cell and gene therapy in the next 10 to 15 years, we're going to see a lot of things properly cured, including cancers. I mean, if you think about CAR-T therapies, which is heavily engineered, so it's engineered cell therapy, I actually believe that we'll see a lot of our current diseases literally wipe off the face of the earth very, very soon.
David Brühlmann [00:16:10]:
Yeah, definitely an exciting future, especially at the pace we're moving these days. A lot of things can happen. As we are wrapping up, what is the question I should have asked?
Yuta Lee [00:16:25]:
The question that maybe you could have asked or that we actually in longevity ask all the time, is there are a lot of billionaires out there, especially today, and a lot of these billionaires would love to live forever, I'm sure, or live much longer, healthier, and they have the means to do it. So why is it that they don't put more of their assets to invest in cell and gene therapy, invest in longevity, or even invest in biotech? That, to me is a big mystery. And the reason why I think it's a mystery is because as you get older, especially if you're over 50 like me, I mean, what does beating the S&P500 for another 10, 15 years mean to you? Absolutely zero. You're going to die.
So unless you do something about it, something's going to go wrong and then you're going to die. So I would highly urge the listeners out there, especially if you have some means, pick your lane, like pick your longevity investment, pick your cell and gene therapy investment and invest in a future of our own health. I'm agnostic about it. I think as long as you pick the topics that are exciting to you or that you're particularly interested in, or maybe a disease that you have in your family lineage, that's all going to help. Because it takes, unfortunately, a lot of money to get through the FDA and the EMA in Europe. And so not only just longevity sector, but the cell and gene therapy sector. We need a lot more focused investment to come in and help. And so your health is the last thing that you can compound, so why not do that? Right? Making more money makes zero sense to me when we're so close.
David Brühlmann [00:17:55]:
Fantastic. Thank you so much for making this case, Yutta. What is the most important takeaway from our conversation you want our listeners to walk away with?
Yuta Lee [00:18:07]:
I want everybody to think about their own health span and their own longevity. I think if we do nothing on longevity, which is how long you actually live, then at least we have to make a big change to health span, how long we are healthy while we're alive. The goal is always live very healthily for a very long time and die very suddenly, because right now, on average, everybody is stuck in bed sick for nine years. And that is a very big price on the healthcare system and only the economics or even the emotional wear and tear on your family. And so we should all be thinking about that and think the easiest things to do are the lifestyle aspects. You always see a lot of influencers out there talking about it. Exercise more and eat better, sleep better. These are cheap things that you can do right now and stay healthy for at least the next five to 10 years.
And I believe that in the next 12 to 15 years, we're going to reach what we call longevity escape velocity. And that just means that you are actively reversing your age by more than one year against your chronological age. So you're technically going backwards. And if you can do that for next five years, you'll probably live another five years. And if you live those five years, there's a really good chance we'll live a very, very, very long and healthy life. So for the listeners out there, start thinking about that. Start easy, do the lifestyle changes. Be healthy, and try and live as long as you possibly can. And you know the big rule that we have in longevity? Don't get hit by a car. No matter how long you could possibly live. You walk in front of a car, you're still going to die, right? Yeah.
David Brühlmann [00:19:49]:
All right, Yuta, let's plan another podcast interview in 50 years from now. And it would be awesome to look back on all the advances.
Yuta Lee [00:19:57]:
That's right. That's very good.
David Brühlmann [00:19:59]:
Well, this has been fantastic. I love your vision. Thank you so much, Juta, for moving the needle in this very important field. Now we have stem cells that are ethically sourced and are very early and this opens so many avenues now and for greater health span and even longevity. It has been fantastic. Thank you so much for coming on the podcast today. Yuta, where can people get a hold of you, learn more about your technology?
Yuta Lee [00:20:27]:
Our website is www.acceleratedbio.com and if you search me on YouTube, if you search me on LinkedIn, you can find me and just get in touch. We are happy to work with everybody because I believe this not only just cell and gene therapy, but longevity is really a community effort and we got to try and bring more and more people into this and be aware of developments. And of course, the more people that know, the more people can stay healthier longer and be happy with their family. So that's the goal.
David Brühlmann [00:20:59]:
Excellent. Thank you so much once again. It was a huge pleasure to have you on today.
Yuta Lee [00:21:05]:
Thank you, David. It was a really, really wonderful time.
David Brühlmann [00:21:08]:
Extending healthspan is one of the most consequential scientific challenges of our time. And today's conversation gave us a grounded look at what that pursuit looks like from the inside. The biology, the strategy, and the long arc of the work. If you found this episode valuable, please leave a review on Apple Podcasts or your platform of choice. It will help other scientists like you discover the show. Thank you so much for joining, tuning in, and I'll see you next time.
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.
If you found value in today’s episode, take a moment to like, follow, and leave a review on Apple Podcasts or your favorite platform—it helps us reach and support more scientists like you.
Thanks for tuning in to the Smart Biotech Scientist podcast and being part of this journey toward bioprocess mastery. For more insights and practical tips, visit www.smartbiotechscientist.com.
About Yuta Lee
Yuta Lee is a biotech entrepreneur and regenerative medicine innovator leading Accelerated Bio. His company commercializes human Trophoblast Stem Cells (hTSCs), a patented platform with strong expansion capacity and natural immune privilege for next-generation cell and gene therapies targeting Parkinson’s disease, Type 1 diabetes, and healthy longevity. Yuta holds degrees from University of California, Berkeley and China Europe International Business School.
Yuta’s background combines biotechnology, business strategy, and intellectual property development, enabling him to bridge scientific innovation with commercialization. His focus is on building scalable regenerative medicine platforms that can support off-the-shelf therapies and help accelerate the transition from personalized medicine to broadly accessible longevity therapeutics.
Connect with Yuta Lee on LinkedIn.
If you’re interested in exploring further the concepts we touched on—such as cell therapy manufacturing, process control, and scaling living therapies—take a look at these related discussions:
Episodes 105 - 106: From Proteins to Cell Therapy: Why ATMPs Aren’t Just Complex Biologics with Oliver Kraemer
Episodes 147 - 148: Lab-Grown Blood: How Stem Cells Transform Transfusions with Ari Gargir
Episodes 179 - 180: How Mesenchymal Stromal Cells Are Transforming Care for Diabetes and Autoimmune Diseases with Lindsay Davies
Episodes 211 - 212: When the Innovator Becomes the Patient: Manufacturing Reality vs. Patient Urgency with Jesús Zurdo
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 secret to healthier, longer lives is quietly discarded in hospital operating rooms every day?
Stem cells offer enormous promise, but the search for the right type—biologically active, ethically sourced, and scalable—has left researchers walking a tightrope for decades. Few realize the story begins in one of medicine’s routine, overlooked byproducts: tissue from ectopic pregnancies.
David Brühlmann welcomes Yuta Lee, founder and CEO of Accelerated Bio, a pioneering biotech company at the forefront of regenerative medicine and longevity.
He was performing surgery on an ectopic pregnancy and he took the mass out. And I was thinking to himself, why are we throwing this away? There's got to be amazing stem cells in here. But to avoid all of the ethical issues that human embryonic stem cells went through, what he ended up doing was scraping off the pre-placental tissue from the outside of it. He handed the rest of the tissue back to the pathologist. And the reason why they can discard it is because in this type of pregnancy, OB-GYNs understand it to be non-viable. Well, my father, having taken the pre-placental tissue from the outside of the embryo, now has the earliest stem cells that you could possibly source without the same ethical concerns.
David Brühlmann [00:00:44]:
What if the key to reversing biological aging was hiding in tissue that medicine routinely discards? My guest today has spent more than two decades pursuing exactly that question. Yuta Lee is the founder and CEO of Accelerated Bio, and he's built a platform around human trophoblast stem cells, the earliest ethically sourced cells from the embryonic stage. Today we explore the biology, the ethics, and the manufacturing logic behind a cell therapy platform targeting age-related decline.
Welcome, Yuta. It's good to have you on today.
Yuta Lee [00:02:36]:
David, happy to be here.
David Brühlmann [00:02:38]:
Share something that you believe about bioprocess development that most people disagree with.
Yuta Lee [00:02:45]:
Ooh, that's a good one—already hitting with the hard questions. Most people disagree with… I think that only people in the industry know how difficult it is to manufacture biological systems. And even if you set all the conditions in the most ideal setting—the ideal SOPs, the ideal parameters—things will still go wrong.
And I think it's almost a myth that a lot of people who are not in manufacturing, processing, or process development believe that once you set the process, it's done and not much can go wrong. But a lot of things can go wrong.
I think the devil really is in the details. And it would be great to have everybody who's not in the manufacturing world, the CMC world, or the process development world understand that this is a really difficult job—because we barely understand biology, and we're trying to control it. And so it's very, very hard to do.
David Brühlmann [00:03:41]:
You're making a great point, and thank you for the reminder. This is something I try to repeat as much as possible to all the people I speak to—manufacturing and development are hard, very difficult.
And it's actually surprising that you are making this statement because you came into biotech with a business background.So tell us your story—what sparked your interest in stem cells, and what were the pivotal and defining moments along the way that led you to becoming a CEO?
Yuta Lee [00:04:15]
Okay, I'm going to start with the story. Actually, the story starts with my father, who is an MD, PhD. His name is Professor Jau-Nan Lee, and he got his MD from Tohoku University in Sendai, Japan, and his PhD at Barts and The London School of Medicine and Dentistry at the University of London. So I actually spent four years of my childhood in London, which was a lot of fun. Then he went back to Taiwan to practice, and he became a key opinion leader in OB-GYN. He sent us off to Los Angeles, and that's why I have this very American accent.
But what happened was, in 2003, he was performing a procedure called a salpingectomy for an ectopic pregnancy. And an ectopic pregnancy happens when the embryo is traveling down the fallopian tube, and sometimes—usually about 1 to 2% of the time—that embryo may become implanted there. And if it implants there, it will grow until it ruptures the tube. And with rupture of the tube, the mother may die from internal bleeding. So it's actually one of the leading causes of death in first-trimester pregnancies.
So to make a long story very short, he was performing surgery—a salpingectomy—for an ectopic pregnancy. And he removed the tissue and was thinking to himself, why are we throwing this away? There's got to be valuable stem cells in here.
But to avoid all of the ethical issues that human embryonic stem cells went through, what he ended up doing was scraping off the pre-placental trophoblast tissue from the outside of it. He handed the rest of the tissue back to the pathologist to confirm the diagnosis—yes, it's an ectopic pregnancy—and then it could be discarded.
And the reason why it can be discarded is because in a normal pregnancy, the embryo needs to implant into the uterine lining, typically by day 7 or day 8. But an ectopic pregnancy is usually not discovered until about 4 to 8 weeks. So there are many weeks in between where that embryo is not properly connected to the uterine environment—there is inadequate support and nutrient exchange. So OB-GYNs understand it to be non-viable. That is the reason why, when you perform this surgery, all you have to do is remove the tissue, send it to a pathologist to confirm, and then it can be discarded.
Well, my father, having taken the pre-placental trophoblast tissue from the outside of the embryo, now has the earliest stem cells that you could possibly source without the same ethical concerns.
And he comes to me two years later—so he discovers this in 2003, and in 2005 he comes to me and goes, “Yuta, you're the business guy. Do something with this.”
To which I respond, “Hey dad, I went to Berkeley for economics and law—what do you want me to do?”
That was pretty hilarious. But as the oldest son, I said, all right, challenge accepted. I’ll take it on and see what I can do with it. Luckily, my brother-in-law was an investment banker at Morgan Stanley in San Francisco, covering healthcare. So I called him up and said, “Bob, who is your go-to IP lawyer for this kind of work?” And he said, “You’ve got to go to Wilson Sonsini in Palo Alto, right next to Stanford—probably the best lawyers for this.”
So we went, found a partner, and filed the first patent. That was the beginning of it all. And five years later, we obtained our first composition of matter patent. And it was so strong that one of the top attorneys called me and said, “Yuta, your patent portfolio—this first composition patent—is very powerful, because you sit between embryonic stem cells and induced pluripotent stem cells, and the non-scalable mesenchymal stromal cells. You have advantages from both sides.” He said, “Keep building out the portfolio. Don’t tell anybody you have this—you’re too early.”
This was 2010, and at that time, no one wanted to invest in stem cells. VCs were not interested. He said, “Keep building the portfolio, but most importantly, do not give this to academic researchers. Because once they publish on it, everything becomes public. And if institutions like Stanford or Harvard file patents, you’ll end up with a patent thicket—and if you ever want to commercialize, you’ll have to license everything back.”
I thought, wow, that is incredible advice. So we went back, I spoke with my parents, and we decided to fund the first 10 years of patent prosecution ourselves as a family. And that’s the reason why we now have access to these early-stage stem cells without the same ethical concerns.
And not only that—we’ve already taken them into Good Manufacturing Practice (GMP) manufacturing, which I’ll talk about in a bit. They also have some very interesting biological characteristics, which I’ll go into as well. But that’s really the beginning of the story. That was 2005 to 2010, and we’ve been working on it ever since. I formed the company Accelerated Biosciences in 2013, and only in 2020—when the topic of allogeneic cell therapy became more prominent—did I start sharing this more publicly. So that's the origin story in a very, very long format.
David Brühlmann [00:09:16]:
What a fascinating story. And it's definitely a non-linear story—really great. Tell us, Yuta, a bit more about stem cells. You mentioned different stem cell types and different stages. Let's start very simple: what are the different types of stem cells, and what is important to watch out for?
Yuta Lee [00:09:37]:
I love that question because this is what most people want to know, right? So I usually explain stem cells in the context of fetal development.
First of all, let’s take it one step back—what is a stem cell? A stem cell is an undifferentiated cell from which all specialized cells originate. Every cell in our body ultimately came from a single original cell. You may already know that every cell in your body contains your DNA within the nucleus.
A stem cell is defined as a cell that can both self-renew—meaning it can generate another identical stem cell—and also differentiate into specialized cells. So it has multiple functional capacities and serves as the origin of all cell types.
Now, relative to where stem cells come from, I usually use a timeline of fetal development. The first eight weeks of our development is the embryonic period. That’s when the sperm and egg fuse, forming a zygote, which then divides and develops. After about eight weeks, development transitions into the fetal period, where organs continue to form and mature. And then, on average, birth occurs around the 38th week. Anything after that is considered adult.
Where do stem cells come from? The earliest stem cells are human embryonic stem cells, typically derived from embryos created via in vitro fertilization (IVF). In IVF, sperm and egg are combined in a dish to create embryos, which are then implanted into the mother. Often, multiple embryos are created, and unused ones may be stored.
In 1998, James Thomson at the University of Wisconsin published the first paper on human embryonic stem cells. He obtained consent to use a donated embryo for research. However, this led to significant ethical concerns, as many people believe embryos represent potential human life. This triggered regulatory restrictions on embryonic stem cell research in many countries. These cells are typically derived at around the blastocyst stage, about five days after fertilization.
Next are human trophoblast stem cells, which are associated with pre-placental trophoblast tissue. These arise during early development and contribute to the formation of the placenta. In our case, these cells are sourced between roughly 4 to 8 weeks post-fertilization—still within the embryonic period.
If you recall my earlier story, in ectopic pregnancies, the tissue is surgically removed. We isolate the trophoblast tissue and return the rest for pathological confirmation and disposal. This is why we describe them as early-stage cells obtained without the same ethical concerns.
After that, during the fetal period (8 to 38 weeks), cells could theoretically be sourced—but this would involve fetal tissue, typically from elective termination, which raises ethical and regulatory challenges, so it is rarely used.
At birth, around 38 weeks, additional biological materials become available: placenta, umbilical cord, cord blood, and amniotic fluid. These are rich in stem cells, particularly mesenchymal stromal cells (MSCs), which are more differentiated and have more limited potential compared to embryonic stem cells.
After birth, in adulthood, stem cells still exist throughout the body. For example, when you cut yourself, stem cells help regenerate tissue. These are adult stem cells, found in various tissues. However, they are harder to isolate. Common sources include adipose (fat) tissue and bone marrow. Bone marrow extraction, for example, requires an invasive procedure.
The last type I want to mention is induced pluripotent stem cells (iPSCs). For those familiar with longevity research, these are very important. They were first described by Shinya Yamanaka at Kyoto University in 2006.
What he showed was that by introducing specific transcription factors (often referred to as the Yamanaka factors) into an adult somatic cell—using methods such as viral vectors like Sendai virus—you can reprogram that cell back into a pluripotent stem cell state. He received the Nobel Prize in 2012 for this work.
This has major implications for longevity and healthspan. If you can reprogram an adult cell back to a more primitive, pluripotent state—essentially a “reset”—then, in theory, cellular aging could be reversed. That’s why many researchers see aging, at least partially, as a biological engineering problem. There are companies like Life Biosciences and Retro Biosciences exploring these reprogramming approaches.
So to recap:
I hope that helps.
David Brühlmann [00:16:19]:
So that means that today, most of the stem cells that are being used clinically are from adult sources, right? That's the large majority today.
Yuta Lee [00:16:31]:
That's right. Most of the research is either using mesenchymal stromal cells (MSCs) or induced pluripotent stem cells (iPSCs). With iPSCs, you reset the cell back to a pluripotent stem cell state, and then you have to differentiate it forward into specific cell types—like neurons, kidney cells, or liver cells.
David Brühlmann [00:16:54]:
Tell us, Yuta, why you are focusing on much earlier cells. What are the advantages of these cells?
Yuta Lee [00:17:01]:
Right—based on that timeline I just mapped out, the general rule is that the earlier you find the cells, the more biologically active they tend to be.
For example, embryonic stem cells can proliferate indefinitely under the right conditions—they are often described as having unlimited self-renewal capacity. You can keep expanding them, although over time they may accumulate genetic changes.
Our cells are also very early, but they are not immortal. They are replicatively senescent, meaning that after a certain number of divisions, they stop proliferating. But because we derive them from such an early developmental stage, we can expand them to about 85 population doublings.
Now, let me explain that. A population doubling is when a cell divides and doubles—so 1 to 2, 2 to 4, 4 to 8, and so on, repeated multiple times. At around 85 doublings, you reach an extremely large number of cells. If you can imagine the number 8 and 25 zeros behind it, that's how many cells you can get from one.
David Brühlmann [00:18:06]:
That’s a lot of cells. So tell us how many that is.
Yuta Lee [00:18:12]:
It’s an enormous number. To give context, the human body has roughly 37 trillion cells. If you expand cells across that many doublings, in theory you could generate enough cells for trillions of individuals from a single donor. So from a manufacturing perspective, scale is effectively addressed. That’s one of the reasons to use earlier-stage cells.
However, even with early cells like embryonic stem cells, regulatory agencies such as the U.S. Food and Drug Administration are cautious. Beyond ethical concerns, there is concern about uncontrolled proliferation.
If you think about it, what other cells proliferate indefinitely? Cancer cells. So introducing cells with high proliferative capacity requires careful control during manufacturing and clinical use. Our cells don’t have that same concern—they undergo cellular senescence, meaning they will eventually stop dividing. But they still offer a high expansion potential, giving you both scalability and a built-in biological limit.
In contrast, MSCs derived at birth—such as from umbilical cord or placenta—typically achieve around 25 to 30 population doublings under standard conditions. That limits scalability.
Another issue is donor variability. If you go back to collect cells from a new donor, you are working with a different genetic background. From a regulatory perspective, that can require additional validation, because it is essentially a new biological starting material.
So this is where earlier-stage cells offer advantages in consistency and scale.
Now, another major advantage of pre-placental trophoblast-derived cells is related to immune interaction. These cells express HLA-G, which is a non-classical major histocompatibility complex molecule involved in immune modulation.
Let me explain it more simply. An embryo contains genetic material from both the mother and the father, so it is partially foreign to the mother’s immune system. Normally, the immune system would recognize and reject foreign tissue.
However, the placenta acts as an interface between the mother and the embryo, and molecules like HLA-G help suppress immune responses locally—essentially signaling immune tolerance.
This mechanism is what allows an embryo to implant and develop. It also enables situations like surrogacy, where a genetically unrelated woman can carry a pregnancy to term.
Because our cells originate from trophoblast-related tissue, they retain some of these immune-modulatory properties.
So if you combine these characteristics:
You get a strong foundation for scalable and potentially broadly applicable cell therapies.
David Brühlmann [00:21:53]:
And I think this also opens the way to allogeneic, off-the-shelf cell therapy treatments, correct?
Yuta Lee [00:22:00]:
That’s right. That’s exactly what we’re aiming for—allogeneic cell therapy. This is also why, even though I founded the company in 2013, I didn’t really talk about it publicly until around 2020. Before that, most of the industry focus—especially with things like CAR-T—was on autologous cell therapy, where you use the patient’s own cells. But now that allogeneic approaches are gaining traction, we have a more complete platform and are ready to share it more broadly. So if you're a researcher or a company thinking about developing an allogeneic therapy, we’d be happy to talk.
David Brühlmann [00:22:34]:
We’ve covered a lot of ground today—from the biology of trophoblast stem cells to the IP fundamentals that underpin Accelerated Bio’s platform.
In part two, we’ll go further into the science of aging itself and what a therapeutic approach to healthspan extension might look like in practice. If this episode added value, please leave a review on Apple Podcasts or your preferred platform. Thank you so much for tuning in—I’ll see you next time.
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.
If you found value in today’s episode, take a moment to like, follow, and leave a review on Apple Podcasts or your favorite platform—it helps us reach and support more scientists like you.
Thanks for tuning in to the Smart Biotech Scientist podcast and being part of this journey toward bioprocess mastery. For more insights and practical tips, visit www.smartbiotechscientist.com.
About Yuta Lee
Yuta Lee is the Founder and CEO of Accelerated Bio, a regenerative medicine company pioneering the use of human Trophoblast Stem Cells (hTSCs) to advance longevity and age-reversal therapies. For more than 20 years, he has focused on developing scalable, ethically sourced cell technologies designed to extend human healthspan and accelerate the future of allogeneic cell and gene therapies.
Under Yuta’s leadership, Accelerated Bio has built a robust intellectual property portfolio with 53 patents supporting commercialization pathways for partners and researchers worldwide. His work is driven by a belief that regenerative medicine should move beyond symptom management toward restoring youthful biological function and making cutting-edge longevity science more accessible to people globally.
Connect with Yuta Lee on LinkedIn.
If you’re interested in exploring further the concepts we touched on—such as cell therapy manufacturing, process control, and scaling living therapies—take a look at these related discussions:
Episodes 105 - 106: From Proteins to Cell Therapy: Why ATMPs Aren’t Just Complex Biologics with Oliver Kraemer
Episodes 147 - 148: Lab-Grown Blood: How Stem Cells Transform Transfusions with Ari Gargir
Episodes 179 - 180: How Mesenchymal Stromal Cells Are Transforming Care for Diabetes and Autoimmune Diseases with Lindsay Davies
Episodes 211 - 212: When the Innovator Becomes the Patient: Manufacturing Reality vs. Patient Urgency with Jesús Zurdo
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
Today we're talking about one of the most frustrating bottlenecks in biosimilar development: how to screen efficiently a long list of quality-modulating compounds when your standard tools — OVAT on one end, a massive DoE on the other — both break down under the weight of the problem.
This is a two-part episode of the Smart Biotech Scientist Podcast. In Part 1, I'll walk you through the problem and the conceptual framework we developed to solve it. In Part 2, we go hands-on: how to actually build this in your lab.
In Part 1: we covered why OFAT takes 12 months and misses interaction effects, why a single large DoE with 17 factors fails under dilution, masking, and combinatorial toxicity — and how the parallel group design sidesteps all three. Plus the three-tool multivariate pipeline: PCA, Mahalanobis distance, decision tree. Today we build it.
Let’s start with compound grouping, because this is where the design logic either holds together or falls apart.
The governing principle: group by biological mechanism, not by convenience.
Four rules.
One: maximum five factors per group.
This is a hard limit. Five factors keeps dilution effects manageable — at most five stock additions per well — and prevents a single dominant compound from masking everything else.
Two: include one to two anchor compounds in every group.
These are well-characterized compounds with documented glycan effects — compounds you trust to give a consistent, interpretable signal. In our study, manganese and asparagine served this role. Their effects are documented across many CHO cell lines, and they provide internal calibration points that enable cross-group comparison, even though the groups are independent experiments.
Three: separate known strong modulators.
If a compound drives a dominant glycosylation effect — for example, a mannosidase inhibitor pushing high mannose to 80–90% — it should be in its own group, or at least be the only potent modulator in that group. Otherwise, its signal overwhelms the others, and you lose most of the information those compounds could provide.
Four: don’t guess unknown mechanisms.
If you don’t know how a compound works, don’t guess. Run a short univariate screen in shake flasks — low, mid, high concentration. Measure the glycan response, then group by response pattern: compounds that increase high mannose together, compounds affecting sialylation together.
A note on scale.
The parallel group method is designed for when your candidate list is too large for a single clean experiment. If you have three to five compounds, run a single multi-factor DoE. With six to nine, use two or three groups. With fewer than three, OFAT or a simple dose–response is the right tool.
Concentration range selection is the step most teams underinvest in, and it’s the one that can invalidate entire experimental groups before you’ve analyzed a single result.
The problem: if your upper concentration level is too high for a potent compound, cells die in those wells. Dead cells produce no glycan data. And because data loss within a group is clustered — all conditions share the same plate — one mis-calibrated compound can corrupt 20–30% of a group’s wells before you’ve run anything.
Three things to do.
One: known potent compounds
For enzyme inhibitors, glycosidase inhibitors, and metabolic modulators, start from published concentration ranges in the literature. Set your upper bound conservatively. You can always extend upward in a follow-up. This is far cheaper than re-running a group.
Two: unknown or poorly characterized compounds
Don’t estimate. Run a preliminary dose–response study in shake flasks: three to five concentration levels over about one week. Identify where growth inhibition begins, then set your DoE upper bound below that threshold. This is the most important preparation step for the entire screen.
Three: pre-qualify osmolality
Multiple stock additions can increase medium osmolality in non-obvious ways. This is a hidden confounder that affects both cell growth and glycosylation independently of compound effects. Measure or estimate osmolality for each condition before the screen, and adjust sodium chloride to bring all conditions back to the same target. We saw this specifically with raffinose, a trisaccharide that meaningfully contributes to osmolality at effective concentrations. We covered this in Episode 227.
A few execution details determine whether your data is trustworthy or misleading.
Evaporation. Use vented lids and validate your evaporation correction. In a 96-well plate, edge wells evaporate more than center wells. This creates position-dependent changes in medium concentration and osmolality, which can generate false biological signals if uncorrected. Measure evaporation rates across the plate layout and incorporate corrections into data processing.
Liquid handling. Use robotics. Stock solution additions are often in the low microliter range, where manual pipetting error can exceed the biological effects you are trying to detect. If robotics are not available, that is a real limitation of the method.
Reference wells. Include replicate reference-condition wells distributed across both plates, not clustered in one area. These provide a noise estimate and a check for positional effects.
Feeding. Your feed schedule must exactly replicate your production process. Changes in feed timing or composition shift the metabolic baseline, which shifts the glycan profile. Without this control, you are comparing against a non-representative system.
Scale-down validation. Before the full screen, run your reference condition in both 96-deep-well format and shake tube format, then compare glycan profiles. If they do not track closely, your screening results will not translate into confirmation experiments. Everything downstream depends on this correlation holding true.
I want to address the statistics barrier directly. You don’t need a bioinformatics background. These tools are already built into standard statistical software most bioprocess labs already have access to.
What you need is to understand what each tool is doing.
PCA. Run it on your full glycan dataset. The goal is to compress your 13-dimensional quality space into two or three dimensions you can visualize on a score plot.
How many components to retain: look at the scree plot — cumulative variance explained versus component number. Keep components up to the natural inflection point. In our study, three components explained 76% of total glycan variance.
Then project the reference product as an external point onto the same score plot. Conditions close to that reference point are your candidates. Conditions far away are not.
Mahalanobis distance. Calculated in your PCA score space. It converts visual closeness into a single number per condition: how far each condition is from the reference product in the full glycan space.
Unlike Euclidean distance, it accounts for correlations between glycoforms — and glycoforms do not change independently.
You then rank all conditions and take the top 20–25%. These are your shake tube confirmation candidates.
Decision tree. Takes your Mahalanobis rankings and identifies which compound levels reliably predict whether a condition ends up in the top group or bottom group.
Two non-negotiable rules: cross-validate (sevenfold is standard) and prune the tree after validation. An unvalidated, unpruned tree will overfit your data and produce rules that only work on the conditions you already ran.
The output you want is simple, interpretable if–then rules you can explain to any stakeholder.
No black box.
Three specific things I would change if I were running this study today.
First: pre-qualify every unknown compound with a dose-response study before the screen. This is about flagging compounds that could cause cell death at your intended concentration range before you commit them to a group. One week in shake flasks, three to five levels. If growth inhibition appears, you adjust your DoE bounds before the screen, not after you’ve lost 30% of a group’s wells. The cost-benefit is clear in retrospect.
Second: measure beyond your primary quality target. We tracked glycan profile as our primary CQA, but we also monitored aggregation and charge variants across all conditions. That broader analytical panel revealed quality effects that glycan data alone would not surface. Whatever your primary target is, add at least two secondary quality readouts from Day 1. You are already running the cells and doing the analytics — the incremental cost is small, and occasionally you find effects that change which condition you advance.
Third: integrate hybrid modeling at two stages. At initial screen design, if you have historical bioprocess data, a hybrid model combining mechanistic understanding with machine learning can predict which concentration ranges and combinations are most informative, allowing you to design a better experiment before running any wells. At the shake tube validation stage, the model identifies the minimum set of conditions needed to confirm results, reducing the number of shake tube runs and therefore analytical cost and workload.
Here's the mindset I want to leave you with.
Process development is an information problem, not a time problem. As you are starting out, you may move slowly because you generate information slowly — one experiment, one question, one answer at a time. The parallel group method changes that by letting you ask multiple questions simultaneously, in the same calendar window as asking one. The multivariate analysis pipeline changes it further: instead of extracting one data point per condition, you extract the full picture — all 13 glycoforms, ranked and explained, against a single target.
The teams that reach IND fastest are not running more experiments. They're running smarter ones. Rational compound grouping, parallel execution, multivariate selection.
This framework extends beyond media optimization. Clone selection, feed development, process characterization, scale-up decisions — wherever you have too many variables for sequential testing and too many interactions for a single large experiment, this logic applies.
Ask better questions. Ask them in parallel. Let the data lead. That's what smarter biotech looks like.
The full peer-reviewed paper: D. Brühlmann et al., "Parallel Experimental Design and Multivariate Analysis Provides Efficient Screening of Cell Culture Media Supplements to Improve Biosimilar Product Quality," Journal of Biotechnology.
Episodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael Sokolov
Episodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist Gernaey
Episodes 99 - 100: From Raw Data to Actionable Insights: Unlocking the Power of Process Models with Fabian Feidl
Episodes 137 - 138: Skip 90% of Bioreactor Runs: The In Silico Revolution in Bioprocess Development with Yossi Quint
If you found value in today’s episode, take a moment to like, follow, and leave a review on Apple Podcasts or your favorite platform—it helps us reach and support more scientists like you.
Thanks for tuning in to the Smart Biotech Scientist podcast and being part of this journey toward bioprocess mastery. For more insights and practical tips, visit www.smartbiotechscientist.com.
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
Today we're talking about one of the most frustrating bottlenecks in biosimilar development: how to screen efficiently a long list of quality-modulating compounds when your standard tools — OVAT on one end, a massive DoE on the other — both break down under the weight of the problem.
This is a two-part episode of the Smart Biotech Scientist Podcast. In Part 1, I'll walk you through the problem and the conceptual framework we developed to solve it. In Part 2, we go hands-on: how to actually build this in your lab.
In the 1990s, the pharmaceutical industry went through a revolution in drug discovery.
Before that shift, chemists were synthesizing and testing candidate molecules one at a time. It was rigorous. It was thorough. And it was slow.
Then high-throughput screening arrived.
The idea was simple: instead of testing one compound at a time, you automate, miniaturize, and parallelize. You screen thousands — sometimes millions — simultaneously. The winning molecules emerged from the data, not from any individual scientist’s intuition. That shift compressed decades of chemistry into years.
Now ask yourself this:
When your cell culture team needs to evaluate 17 potential quality-modulating compounds for a biosimilar development program, how are they doing it?
One compound at a time?
Or one massive design of experiments with all 17 factors thrown in together?
If either of those is the answer — you're leaving months on the table, and you're likely missing the biological interactions that matter most.
This episode is about borrowing that same spirit of intelligent parallelization, applying it to cell culture media optimization, and getting you to your answer in two rounds of experiments.
Let me set the scene. You're developing a biosimilar monoclonal antibody. Your glycan profile doesn't match the reference product — high mannose content is off, galactosylation doesn't align, fucosylation is out of range. These are your critical quality attributes, and the gap needs to close.
You do your homework. You review the literature, consult your colleagues, look at what compounds have been shown to modulate glycosylation in CHO cells. At the end of that exercise, you have 17 candidate quality-modulating compounds. Not two. Not five. Seventeen.
Option A is one-variable-at-a-time — OVAT. Test each compound independently, one at a time. This is intuitive and simple to execute. It also takes many experiments, and it will miss every interaction effect. In a system as complex as CHO glycosylation, those interactions are not a secondary concern. They're often the whole story.
Option B sounds more rigorous: one large design of experiments with all 17 factors. Statistically designed, combinatorial. In theory, you capture everything at once.
In practice, it has three fatal flaws.
First: dilution effects. Adding 17 stock solutions to your medium changes the total volume. That dilutes everything else — basal medium, glucose, glutamine. Your model is trying to interpret compound effects against a background that's constantly shifting. Signal degrades.
Second: combinatorial toxicity. Without prior concentration qualification for each of your 17 compounds, some combinations will be toxic. Cells die, wells fail, data disappears — in a clustered, non-random way. With 17 unqualified factors, data loss may jeopardize your ability to draw any conclusions from your experiment.
Third: masking. If one dominant modulator is in the mix — a mannosidase inhibitor pushing high mannose to 90 percent — that signal drowns out the more subtle effects of the other 16 compounds. The candidates that might give you fine-tuned control never surface.
We ran into all three of these problems. And that's what forced us to develop a different approach.
The core idea: instead of testing all 17 compounds together, split them into five parallel experiments, each with five factors or fewer. Then — critically — run all five experiments at the same time.
This is not a compromise between OVAT and a single large DoE. It is strictly better than both, simultaneously.
Here's how we built the groups. The governing principle: group by biological mechanism, not by convenience.
Group 1 and Group 2: Groups 1 and 2 contained the high mannose modulators. Group 1 held the Golgi processing sugars — compounds like raffinose and GlcNAc that affect mannosylation through osmotic and metabolic mechanisms. Group 2 held the mannosidase inhibitors — compounds that directly block enzymatic trimming of high-mannose glycan structures.
Group 3: targeted sialylation and charge variant modulators.
Group 4: targeted fucosylation and galactosylation drivers.
Group 5: contained growth promoters — compounds that affect culture performance and modulate glycosylation indirectly through metabolic changes.
In every group, we included two anchor compounds: manganese and asparagine. These are well-characterized modulators with documented effects across many CHO cell lines. They served as internal calibration references, allowing us to compare results across groups even though each group was an independent experiment.
All five experiments ran simultaneously in 96-well deep-well plate fed-batch cultures, using robotic liquid handling.
Why is this strictly better?
On dilution: you're adding at most five stock solutions per well instead of 17. Dilution effects are minimal.
On masking: if swainsonine is pushing high mannose to 90 percent in Group 2, it only masks the other four compounds in Group 2. Groups 3, 4, and 5 are completely unaffected.
On calendar time: all five groups run in parallel. Elapsed time is identical to running a single experiment.
You get the biological focus of a small experiment and the candidate breadth of a large one. At the same time, in the same calendar window. In other words, the math works out in your favor on every axis: less time, better signal quality, and better interpretability.
After a screen like this, you may be tempted to look at one or two key glycoforms — high mannose, or G0F — pick whatever condition showed the best result, and move on. That approach is using about 10 percent of the information your screen just generated.
We measured 13 glycoforms. In biosimilar development, every glycoform is potentially relevant to your CQA specification. Improving three glycoforms while worsening five others doesn't bring you closer to the reference product — it might push you further away on the attributes you didn't check.
To optimize all 13 glycoforms simultaneously toward the reference product profile, you need multivariate statistics. We used three tools in sequence.
The first is PCA — principal component analysis.
PCA compresses your 13-dimensional glycan dataset into two or three dimensions you can visualize. Every experimental condition becomes a point on a score plot. Conditions with similar full glycan profiles cluster together; conditions that differ are separated.
The key move: you project the reference product as an external point onto that same score plot. Now you have a map, and the reference product is the target marked on it. You can see — visually — which conditions are close to where you need to be and which are not.
In our study, three principal components captured 76 percent of the total glycan variance. Conditions containing 2F-peracetyl-fucose clustered far from the reference product target. Conditions containing raffinose clustered closest to it. For the first time, we could see the entire quality landscape in a single picture.
The second tool is Mahalanobis distance.
PCA gives you a map. Mahalanobis distance gives you a number: the multivariate distance from each experimental condition to the reference product target. The lower the number, the better the glycan match.
Unlike simple Euclidean distance, Mahalanobis distance accounts for the correlation structure between glycoforms — and glycoforms are biologically correlated. If one structural class changes, others tend to move predictably. Mahalanobis distance treats those correlations correctly, making it a more accurate measure of how close you actually are to your target profile.
You rank all conditions from lowest to highest distance. The top 20 to 25 percent — the conditions with the lowest Mahalanobis distance — become your confirmation candidates. The selection is objective, data-driven, and fully defensible.
In our paper: conditions with raffinose consistently ranked closest to the reference product. Conditions with 2F-peracetyl-fucose ranked furthest.
The third tool is a decision tree.
You now know which conditions performed best. The decision tree tells you why. It takes your Mahalanobis rankings as input and generates a hierarchical set of if-then rules — which compound, at which concentration level, most reliably drives conditions toward the top of the ranking.
Two rules that are non-negotiable. First: always cross-validate. Sevenfold cross-validation is standard — you partition your data, train and test iteratively, and ensure your rules hold up on data the model hasn't seen. Second: prune the tree. An unpruned tree overfits your specific dataset and gives you rules that don't generalize.
The output is a set of interpretable decision rules you can read out loud, explain to your quality team, and defend to regulatory reviewers. No black box. Every branch is understandable and traceable.
Three group winners emerged: raffinose from Group 1, galactose from Group 4, Enhancer 2 from Group 5 — combined with a temperature shift to 33 degrees Celsius.
In shake tube confirmation: 75 percent of confirmation conditions outperformed the best 25 percent of the initial 96-well screen.
Two rounds of experiments. Estimated time savings of three to six months. Quality testing cost reduction of more than 50 percent.
One honest caveat before I close Part 1.
The statistical tools we used — PCA, Mahalanobis distance, decision trees — were the right tools in 2017. They still work. But if I were running this study today, I would add one more layer: hybrid modeling.
Hybrid modeling combines mechanistic knowledge of your bioprocess with machine learning trained on experimental data. Applied to this workflow, it can do two things we didn't do in the paper. First, it can help design a smarter initial 96-well screen by predicting which concentration ranges and combinations are most informative, based on historical bioprocess data you already have. Second, it can minimize the confirmation experiments needed to validate your screen results.
I've covered hybrid modeling in depth with several guests. Michael Sokolov — co-author on this paper — walked through the fundamentals in Episodes 5 and 6. Krist Gernaey took it further into digital twin territory in Episodes 173 and 174. Fabian Feidl covered the practical side in Episodes 99 and 100, and Yossi Quint in Episodes 137 and 138. All of those are linked in the show notes.
In Part 2, we go hands-on. How to design your compound groups by biology. How to set concentration ranges without losing data. How to run the 96-well screen with the rigor this method requires. And three things I would do differently if I were running this study today.
The full peer-reviewed paper: D. Brühlmann et al., "Parallel Experimental Design and Multivariate Analysis Provides Efficient Screening of Cell Culture Media Supplements to Improve Biosimilar Product Quality," Journal of Biotechnology.
Episodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael Sokolov
Episodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist Gernaey
Episodes 99 - 100: From Raw Data to Actionable Insights: Unlocking the Power of Process Models with Fabian Feidl
Episodes 137 - 138: Skip 90% of Bioreactor Runs: The In Silico Revolution in Bioprocess Development with Yossi Quint
If you found value in today’s episode, take a moment to like, follow, and leave a review on Apple Podcasts or your favorite platform—it helps us reach and support more scientists like you.
Thanks for tuning in to the Smart Biotech Scientist podcast and being part of this journey toward bioprocess mastery. For more insights and practical tips, visit www.smartbiotechscientist.com.
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
T cell therapies promise life-changing outcomes for cancer patients, but their personalized, patient-specific nature throws a wrench into the classic centralized manufacturing playbook. These aren’t one-size-fits-all treatments; they’re batch-of-one, demanding fresh thinking on every front—from process development to regulatory approvals and global access.
In this episode of the Smart Biotech Scientist Podcast, host David Brühlmann is joined by Chantale Bernatchez from CTMC, a joint venture between Resilience and MD Anderson Cancer Center. Building on their previous discussion, the conversation delves into the intricacies of cell therapy manufacturing, the importance of local models to ensure global access, and the evolving landscape of next-generation therapies.
Cell therapies are different from typical pharmaceutical products, where one batch can be used to treat many patients. In cell therapy, one batch is needed for each patient. Because of this, a centralized manufacturing model—where a single facility supplies the entire world—does not function well.
Cell therapies benefit from more localized manufacturing. Accessibility to these therapies in many parts of the world has been challenging. We believe that we can transfer our technology to centers in different regions that currently lack access to cell therapy, and support them every step of the way to help make these treatments more available globally.
David Brühlmann [00:00:45]:
Welcome back. In Part One, Chantale Bernatchez from CTMC—a joint venture between Resilience and MD Anderson Cancer Center—walked us through the science and complexity behind T cell therapies, and how CTMC builds processes that respect biology rather than fighting it.
Now we go further. We’ll talk about next-generation cell therapies, the realities of global technology transfer, and the manufacturing barriers that still stand between these treatments and the patients who need them.
Let’s pick up where we left off.
I’d like to zoom out now, Chantale, and look at how you operate. You work in a unique setting—you embrace a patient-adjacent manufacturing model that is directly embedded within MD Anderson. Can you tell us how this changes the way you develop processes, and how it enables you to move faster into the clinic?
Chantale Bernatchez [00:03:08]:
Yes. I would say that our close collaboration with MD Anderson Cancer Center is key. We are physically in close proximity, and we also have strong collaboration with the clinical teams there, as well as with regulatory interactions with the FDA.
For first-in-human studies, our regulatory team files the IND on behalf of MD Anderson in electronic format with the FDA. We’ve had multiple touchpoints with the FDA across different products—filing pre-INDs, INDs, and IND amendments for both engineered and unengineered TILs, as well as different CAR-T modalities.
This has helped us understand, from a process perspective, what the FDA expects at different stages of clinical development. Because of our history in T cell therapy development at MD Anderson—and the fact that many members of our team transitioned from MD Anderson to CTMC—we are essentially continuing work that started there.
We’ve also built strong logistical workflows with MD Anderson for acquiring starting materials, such as apheresis products or tumor tissues, and returning the final product. This allows us to accelerate study activation timelines after IND clearance.
From a process development standpoint, we’ve accumulated experience across multiple platforms tailored to different products. This makes it easier to evaluate the needs of a new product and adapt existing platforms to accommodate it.
Having worked with diverse patient populations and cell therapy modalities, it becomes easier to navigate new challenges. At the same time, we need to stay current with innovations in instruments and reagents to enable next-generation therapies.
At CTMC, we focus on what we call “bioinnovation”—tracking new trends and identifying technologies that can improve our processes and support our partners. For example, many of our products use retroviral transduction, which can be advantageous for process closure. However, retroviruses require actively proliferating cells for efficient integration. Achieving that level of activation in a closed system can be challenging.
We’ve collaborated with innovative partners who have developed solutions to these challenges, and we’re now incorporating these technologies into updated versions of our platforms. Overall, it’s been very exciting to work closely with technology and biopharma partners to continuously improve the quality of the processes we deliver.
David Brühlmann [00:06:12]:
What are the key success factors for this model? Is it the close proximity that allows faster development? Access to knowledge? Or something else?
Chantale Bernatchez [00:06:24]:
It’s a combination of factors. We’ve developed a model that is somewhat different from a traditional CDMO. It’s highly collaboration-based. We aim to actively add value to the products we work on.
For that reason, we often collaborate with smaller companies that may not yet have fully defined processes. We can help them navigate process development challenges so they don’t have to reinvent the wheel.
We have deep expertise in both TIL and CAR-T manufacturing. So when a new product comes in—for example, a TIL or CAR-T therapy involving gene addition or gene knockout—we can usually integrate it into an existing platform process.
This allows us to save significant time in transitioning from a research-grade process to a GMP-compliant process suitable for early-phase clinical trials. Because of our experience in bringing these types of products from research to clinical proof of concept, we can help accelerate development timelines quite effectively.
David Brühlmann [00:07:26]:
In addition to that, you are working on an alliance that aims to transfer cell therapy manufacturing knowledge globally. How does that work?
Chantale Bernatchez [00:07:35]:
Yes, this program really stems from the realization that cell therapies are fundamentally different from traditional pharmaceutical products. As I mentioned before, one batch is needed for each patient. Because of this, a centralized manufacturing model—where one facility supplies the entire world—does not work well.
Cell therapies benefit from local manufacturing. While in the U.S. there are multiple CAR-T products approved, and now a TIL therapy product as well, access to these therapies in other parts of the world has been much more limited.
We believe that we can transfer our technology—our platform processes for TILs, for example—to centers in different regions that currently do not have access to these therapies. We already have a first partner in this alliance in Brazil, and we intend to work very closely with them to understand their specific needs. These collaborations are highly customized depending on each center. We are equipped to transfer both our manufacturing processes and analytical methods. We can assess where a partner stands and support them in engaging with their local regulatory authorities, which may have less experience reviewing cell therapy applications compared to agencies like the FDA.
We can help define a regulatory strategy, support interactions with regulators, and provide hands-on training in both process development and analytics. Essentially, we aim to support our partners every step of the way, with the goal of making these therapies more accessible globally. We’ve seen strong interest in this model, and we hope to expand it to additional centers over time.
David Brühlmann [00:09:31]:
What are you seeing as you work on this model? What are the biggest manufacturing and process barriers that currently limit patient access?
Chantale Bernatchez [00:09:41]:
The global alliance network is really designed to expand access to these therapies.
What we’re seeing is that many centers recognize the clinical benefits of cell therapies and want to adopt them, but they are often unsure where to begin.
TIL therapy, in particular, involves more complex manufacturing compared to CAR-T. As I mentioned earlier, it requires longer production timelines and much larger cell numbers. This makes it more challenging to implement. Many centers don’t know how to set up the process, what infrastructure is needed, or which reagents are critical.
This is where we can help. Over the years, we’ve developed optimized methods to grow cells even from heavily pre-treated patients—something that took significant time and experience to achieve.
By transferring these established processes, we allow partner centers to bypass many of the initial challenges and accelerate their ability to successfully manufacture these therapies from the start.
David Brühlmann [00:10:38]:
For smart biotech scientists who are unsure where to start, what is one piece of advice you would give them?
Chantale Bernatchez [00:10:46]:
I think for someone starting out, one important aspect is gaining exposure to GMP manufacturing environments.
In my team, I have the privilege of working with scientists who understand the realities of GMP manufacturing because they have that background. For a process development scientist, it is extremely valuable to understand the constraints of that environment.
What may seem feasible or straightforward in a research or process development lab may not be suitable for a GMP setting. So it’s important to keep those constraints in mind at every step when developing a new process.
Beyond that, challenges will inevitably arise. The key is to be patient but persistent—there is always a way to overcome them.
We’ve brought several processes into the clinic that encountered difficulties at some point, required further optimization, and went through iterative cycles of improvement. It’s very much a back-and-forth process.
Every product is different, and new technologies require new adaptations. For example, in the CAR-T space, there are now very innovative approaches, such as logic-gated CAR-T cells that can target more than one antigen.
These are likely to be the next wave of successful therapies in the clinic. But each new generation requires corresponding adaptations in process development to meet these new biological and technical realities.
David Brühlmann [00:12:08]:
Speaking of the future, Chantale, how do you see the future of CAR-T therapies? We’re hearing about second- and third-generation TIL and CAR-T approaches—what does that mean, and where is the field heading?
Chantale Bernatchez [00:12:24]:
Yes, so for CAR-T specifically, we’ve already seen several generations of these therapies. As you mentioned, the constructs have been iterated over time—starting with enhancing signaling through the CAR molecule in second-generation designs, and then adding additional elements to improve function within the tumor microenvironment.
More recently, we are seeing approaches aimed at broadening the range of antigens targeted by CAR-T cells while incorporating safety mechanisms. As I mentioned earlier, one of the main challenges is finding a single antigen that is exclusively expressed on tumor cells and not on normal tissues.
To address this, newer “logic-gated” CAR-T designs are being developed. These cells can recognize multiple antigens but will only become fully activated when specific combinations of antigens are present—typically those found on tumor cells.
For example, companies like Link Cell Therapies are developing CAR-T products that target two antigens—ENPP3 and CA9—which are co-expressed on renal cell carcinoma tumor cells. While each antigen may also be found individually on normal cells, they are not co-expressed there. The CAR-T cells are designed to become fully activated only when both antigens are present together, which helps restrict cytotoxicity to tumor cells and reduce off-target effects.
These next-generation designs aim to minimize toxicity while expanding the range of patients who could benefit from CAR-T therapies.
On the TIL side, the currently approved therapy is still unengineered and relies on the natural ability of T cells to recognize tumors. However, next-generation TIL therapies involve genetic engineering to further enhance their function.
At CTMC, we have a long-standing collaboration with Obsidian Therapeutics, which is developing a membrane-bound IL-15 engineered TIL product. The goal is to eliminate the need for high-dose IL-2 administration, which has traditionally been required to support TIL engraftment and persistence but is associated with significant toxicity.
By engineering T cells to express IL-15, the cells can support their own survival and function without external cytokine supplementation, potentially reducing toxicity and improving efficacy. Early data are promising, although studies are still based on relatively small patient numbers.
Another example is KSQ Therapeutics, which is developing CRISPR-edited TIL products. In this approach, genes that limit T cell activity are knocked out to enhance their function. For instance, they are targeting genes such as SOCS1 and Regnase-1, which act as negative regulators of T cell activity.
Overall, the field is clearly moving toward enhancing the functionality of infused T cells—both in CAR-T and TIL therapies. For CAR-T, shorter manufacturing processes that preserve T cell fitness are a major focus and appear very promising.
Another emerging concept is in vivo CAR-T, where the ex vivo manufacturing step is eliminated entirely. Instead, patients are infused with a viral vector that directly engineers their T cells inside the body.
While this approach is very promising, it also presents challenges. With traditional ex vivo manufacturing, we can tightly control key parameters—such as cell dose, number of viral integrations, and product purity. These safety controls are more difficult to implement with in vivo approaches.
Although strategies are being developed to target the viral vector specifically to T cells, it still needs to be demonstrated that off-target cells—or even tumor cells—are not inadvertently engineered.
So while in vivo CAR-T is an exciting and potentially transformative approach, there is still a lot to learn before it can become a mainstream therapeutic option.
David Brühlmann [00:16:48]:
Before we wrap up, Chantale, what burning question haven’t I asked that you’re eager to share with our biotech community?
Chantale Bernatchez [00:16:58]:
I think CAR-T and TIL therapies are here to stay as treatment modalities. I’m very hopeful for the future. We are seeing increasingly effective therapies, and I think the future is bright. Having worked in this field for quite some time, I’m very excited to see these therapies coming to fruition and achieving so much success. I’m not sure what question I would ask you.
David Brühlmann [00:17:25]:
Excellent. This has been great, Chantale. We’ve covered a lot of ground today. If our listeners had to take away just one single thing, what would that be?
Chantale Bernatchez [00:17:39]:
I think the field has come a long way. We now have regulatory approvals for both CAR-T and TIL therapies, and many major hurdles have been overcome. I see strong interest in expanding access to these therapies into other geographies, and I truly hope that in the coming years we see acceleration in global availability—so that patients everywhere can benefit from these treatments.
David Brühlmann [00:18:09]:
Where can people connect with you and learn more about your work—and hopefully also get inspiration from your novel model?
Chantale Bernatchez [00:18:20]:
We can connect on LinkedIn. I’m happy to chat about our model. People are also welcome to stop by in Houston at CTMC. We’re always open to connecting and exploring how we can help develop new cell therapy products or share insights about our approach. I believe the model we are developing—based on comprehensive partnerships with early-stage cell therapy developers—is something the field really needs.
The traditional fee-for-service model has limitations. Our approach can help early developers avoid missteps, accelerate development, and ultimately reach patients faster by enabling earlier clinical testing of new therapies.
David Brühlmann [00:19:08]:
There you have it, Smart Biotech Scientist. Take this opportunity to reach out to Chantale—you’ll find the links in the show notes. And thank you very much, Chantale, for being on the show today and sharing your passion for cell therapies and your perspective on where the field is heading.
Thank you so much.
Chantale Bernatchez [00:19:28]:
My pleasure. Thanks for having me.
David Brühlmann [00:19:30]:
From next-generation cell therapy approaches to global manufacturing networks, Chantale Bernatchez has given us a clear-eyed view of where this field is heading and what it will take to get there.
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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Thanks for tuning in to the Smart Biotech Scientist podcast and being part of this journey toward bioprocess mastery. For more insights and practical tips, visit www.smartbiotechscientist.com.
About Chantale Bernatchez
Chantale Bernatchez is a pioneer in the field of cancer immunotherapy, with a career dedicated to advancing T-cell–based treatments for solid tumors. After joining MD Anderson Cancer Center in 2007 for her postdoctoral training in tumor immunology, she became a key contributor to adoptive T-cell therapy programs, particularly in the development and manufacturing of tumor-infiltrating lymphocyte (TIL) therapies. Her work helped demonstrate meaningful clinical responses in patients with metastatic melanoma, including long-lasting remissions.
From 2013 to 2020, Chantale led a research laboratory focused on enhancing TIL therapies through improved processes and functional optimization. She later transitioned into the biotech sector and now leads Process Development at CTMC, where she drives innovation at the intersection of academic research and industrial manufacturing. Working within a collaboration between MD Anderson and National Resilience, she continues to shape the future of CAR T and TIL therapies while mentoring teams and advancing next-generation cancer treatments.
Connect with Chantale Bernatchez on LinkedIn.
If you’re interested in exploring further the concepts we touched on—such as cell therapy manufacturing, process control, and scaling living therapies—take a look at these related discussions:
Episodes 105 - 106: From Proteins to Cell Therapy: Why ATMPs Aren’t Just Complex Biologics with Oliver Kraemer
Episodes 109 - 110: Spinning Like Earth: Designing Low-Shear Bioreactors for Better Cell Culture with Olivier Detournay
Episodes 125 - 126: How to Enhance Cell Engineering Using Mechanical Intracellular Delivery with Armon Sharei
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
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Cell and gene therapies represent a revolutionary advance in personalized medicine, offering hope where traditional biologics and small molecules have struggled. Yet, these groundbreaking treatments bring unique manufacturing complexities that the industry is only beginning to truly appreciate.
David Brühlmann sits down with Chantale Bernatchez, Head of Process Development at CTMC. With two decades of experience, four patents, and a career spanning both academic discovery and industry innovation, Chantale has lived through the rapid evolution of T cell therapies—from academic cleanrooms to manufacturing at scale for game-changing clinical trials.
Her work bridges CAR-T technology, tumor-infiltrating lymphocytes, and next-generation cell engineering—always with an eye on the practical realities of process development.
The most underappreciated parameter in cell therapy process development, to me, is the variability of the starting material—whether it be tumor or apheresis material—which can lead to a large difference in T cell quality, for example between growing T cells from normal donors or patient samples. Moreover, maybe that's the most underappreciated factor: when you have heavily pre-treated patient samples, they can behave very differently than even other patient samples. So applying the same process to variable samples will give you a variable outcome—a reality that we need to live with every day.
David Brühlmann [00:00:41]:
Cell therapy has moved from a promising concept to clinical reality. But behind every successful T cell therapy lies a manufacturing challenge of extraordinary complexity. Today I'm joined by Chantale Bernatchez, who is the Head of Process Development at CTMC, a joint venture between Resilience and MD Anderson Cancer Center. Chantale is a 20-year veteran of T cell therapy with four patents in adoptive cell therapy. What does it actually take to turn a patient's own immune cells into a consistent, scalable, life-saving treatment? Let's find out.
Chantale, welcome. It’s good to have you on today.
Chantale Bernatchez [00:02:42]:
It's a pleasure to be here. Thanks for having me.
David Brühlmann [00:02:45]:
Chantale, share something that you believe about bioprocess development that most people disagree with.
Chantale Bernatchez [00:02:52]:
I will go with what I think is the most underappreciated parameter in cell therapy process development, which to me is the variability of the starting material—whether it be tumor or apheresis material—that can lead to a large difference in T cell quality, for example between growing T cells from normal donors or patient samples. And moreover, maybe that's the most underappreciated factor: when you have heavily pre-treated patient samples, they can behave very differently than even other patient samples. So applying the same process to variable samples will give you a variable outcome.
And I would say this is a big takeaway, although it seems simple. But after working in process development for many years with different patient populations or normal donor samples, that’s a reality that we need to live with every day, and I think it’s really important—and maybe underappreciated.
David Brühlmann [00:03:50]:
And this is an important point to remember, especially if you're listening and you work in biologics, where the ballgame is very different. In cell and gene therapy, you have this huge variability because every donor, every patient is different.
Before we dive further into the science and the technology, I would love to hear about your story. Chantale, can you draw us into your journey? What first sparked your interest in cell therapy, and what were some key moments along your long career that led you to your current role?
Chantale Bernatchez [00:04:25]:
Absolutely. So I grew up in Quebec, Canada—I’m Canadian. In college, I was interested in biomedical sciences. I eventually did a PhD in immunology, so I am an immunologist by training. I studied the development of T cells in the thymus. And although I thought it was fascinating, I decided that my next career step should use my knowledge in immunology and apply it to a therapeutic field.
For personal reasons, I found myself moving to Houston, Texas, as my husband had found work there. For my postdoctoral training, I was in very close proximity to MD Anderson Cancer Center, which is the largest cancer care center in the U.S. I thought it was a great opportunity to combine my deep training in immunology with applying it to finding a cure for cancer.
So I joined a tumor immunology laboratory at MD Anderson in the Department of Melanoma Medical Oncology. This was in 2007, when I started my postdoctoral training. I first worked on a project involving vaccination against cancer, but quickly joined a new program that was just starting at the time, which utilized cell therapy to treat cancer.
This program was initiated by Dr. Patrick Hwu, and it used tumor-infiltrating lymphocytes (TILs) to treat metastatic melanoma patients. I played various roles in this program over the 15 years that I spent at MD Anderson—first overseeing the production of T cells in GMP cleanrooms, and then moving on to opening my own research lab and trying to understand the correlates of response—why some patients respond to therapy and others don’t.
At the same time, I worked on expanding the number of patients eligible for this therapy by overcoming some manufacturing challenges. Even in my research lab, I was doing what we now call process development in industry. We were trying to more consistently derive T cells from tumors, even those with lower infiltration than melanoma—for example, pancreatic cancer.
As we started treating more and more melanoma patients, we had a lot of success with the therapy—up to a 50% clinical response rate. So half the patients benefited clinically, which was great. However, as more therapies became available—such as other immunotherapies, particularly checkpoint blockade strategies, and small molecules like BRAF inhibitors—patients had more FDA-approved treatment options. As a result, they came to clinical trials later, with more advanced disease.
This meant we were receiving samples from increasingly heavily pre-treated patients. We observed a steady decline in our ability to grow TILs from these tumors using the historical methodology developed at the NIH by Dr. Steven Rosenberg, where Dr. Hwu had trained before bringing the approach to MD Anderson.
So we had to optimize our methods to counteract this phenomenon and ensure we could still derive enough T cells for therapy for most patients. Gradually, we introduced manufacturing changes to improve this.
Then we turned our attention to genetic engineering of T cells. We wanted to enhance their functionality in the tumor microenvironment. We explored different strategies, such as introducing a dominant-negative receptor for TGF-beta to block its suppressive activity on T cells, or adding molecules to improve T cell migration to tumors.
Over my time at MD Anderson, I contributed to advancing cell therapy in multiple ways. When our current CEO, Jason Bock, came to MD Anderson and created a new department focused on developing cell therapy products in a more industrialized way, I was very interested in joining. It felt like a natural progression.
About a year and a half after the department was created, it transitioned into a separate entity—a joint venture between MD Anderson and Resilience, a manufacturing company. Again, I followed that trajectory, continuing to develop TIL products while also expanding into CAR-T therapies, another major T cell therapy modality that has seen more success in hematologic cancers.
Now, we’re fortunate to collaborate with both industry and academic partners developing innovative therapies. I believe we can make a real difference in shortening the time it takes for these therapies to reach clinical proof of concept. That’s really the mission of this group—to accelerate the development of these highly innovative treatments.
David Brühlmann [00:09:34]:
Yeah, that’s exciting. And what I’m also hearing is that there has been a lot going on in cancer therapies over the past several years. We now have different therapeutic options at our disposal.
Can you help us understand what fundamentally changes when we use cell-based immunotherapies—specifically using the patient’s own immune cells? You mentioned T cells and CAR-T cells. What are these cells, and how are they different?
Chantale Bernatchez [00:10:01]:
Okay, so the difference between traditional medicines like chemotherapy—which are small molecules—and cellular therapies can be understood like this: for small molecules, the manufacturer produces one batch of a therapeutic that can treat a large number of patients from that same production batch.
For cell therapy, the treatment starts with the patient and ends with the patient. Meaning we obtain either apheresis material for CAR-T cells or tumor tissue for tumor-infiltrating lymphocytes (TILs) from the patient, expand these cells ex vivo, modify their properties, and then reintroduce them into the same patient.
So one batch is now equal to one patient. That’s a very important difference. There’s a lot that goes into releasing each batch. For autologous therapies—meaning therapies where the cells come from a patient and are returned to that same patient—there is the same level of effort required for batch release, but each batch treats a different patient.
Also, because these are living therapies, they are not chemically defined products that you can simply synthesize using a machine. These therapies require special care during manufacturing. You need to optimize the conditions, and because your source material varies—since every batch starts from a different patient—applying the same process to a variable starting material will result in a variable final product.
The role of process development is to design a process that works for most, if not all, patients—finding conditions that are robust despite that variability.
David Brühlmann [00:11:42]:
And I imagine that’s quite a difficult undertaking because you have so much inherent variability. What are some strategies you’re using to develop this standard process?
Chantale Bernatchez [00:11:54]:
Yes, definitely—this is a challenge. As I mentioned earlier, for CAR-T cell therapies, because several generations of these therapeutics have already been developed, newer protocols now often recruit patients who have already progressed after a prior line of CAR-T therapy.
So you can imagine these patients have already undergone lymphodepletion and T cell therapy, and now they’re coming back for another CAR-T product. Their immune system has already been significantly challenged, and we’ve found that growing these cells in the laboratory is more difficult.
So we’ve adapted our methodologies—for example, by changing how we activate the cells or adjusting the duration of the process—to ensure we maintain or impart good functionality to these cells.
I would say that depending on the starting patient population, these adaptations will vary. This applies both to T cells derived from tumors and those derived from blood.
It has really been a challenge to keep up, because as a product matures, the patient population it targets may change. So in some cases, you need to update your process to continue robustly expanding cells from these patients.
Thankfully, as the cell therapy field evolves, there is also innovation in the reagents we use. We’ve seen developers introduce new types of reagents that are better suited for expanding cells from heavily pre-treated patients. That has been very helpful.
We need to continuously monitor and evaluate innovations in this area as well.
David Brühlmann [00:13:37]:
How do you adapt your process to the specific cell population you’re working with? Do you adjust certain process parameters—such as temperature or other culture conditions—or do you add specific components? What is your strategy there?
Chantale Bernatchez [00:13:57]:
So mainly, for decreased cell fitness coming from more heavily pre-treated patients, what we have found to be most useful is increasing the activation strength and providing better co-stimulation to the cells.
For T cells to be fully activated and able to proliferate, they need to be stimulated through their T cell receptor (TCR) and also receive a co-stimulatory signal. These co-stimulatory molecules can vary depending on the subtype of T cells you are working with.
For example, in melanoma, in our tumor-infiltrating lymphocyte (TIL) program, we initially observed that patients receiving higher proportions of a certain T cell subtype—CD8+ T cells—responded better to therapy. So we modified our culture conditions to enrich for CD8+ T cells in our final product.
To achieve this, we used TCR activation with an agonistic molecule, and we also added 4-1BB (CD137) agonistic stimulation during the early phase of TIL expansion, along with IL-2 to provide cytokine support. This approach streamlined several aspects of the process. It reduced manufacturing time, increased the yield of T cells, and increased the frequency of CD8+ T cells.
As I mentioned earlier, with heavily pre-treated patients, we saw a decline in our ability to grow TILs. Toward the end of my time at MD Anderson—around 2019, after multiple therapies had been FDA-approved and widely used in melanoma—we saw our success rate in growing enough cells for therapy drop to about 50% of patients.
After implementing our new method, we were able to bring that success rate back up to 95%. That was very significant for us.
It also allowed us to expand beyond melanoma. Melanoma is typically highly infiltrated with immune cells because it is very immunogenic, often due to mutations caused by sun exposure. But with these same process improvements, we were able to derive T cells more easily from smaller tumor samples and from tumors with lower T cell infiltration, such as pancreatic cancer.
This allowed us to move from one indication to another without needing to further modify the process, which is very valuable.
David Brühlmann [00:16:22]:
What are the typical volumes you work with, and how long does a production run last?
Chantale Bernatchez [00:16:28]:
In general, it depends heavily on the specific process. On the CAR-T side, historically, manufacturing processes lasted around 7 to 10 days. However, what we’ve learned in recent years is that shorter processes better preserve T cell fitness and maintain a higher proportion of less differentiated, more “naive-like” T cells.
As a result, many groups have moved toward shorter processes—around 3 to 5 days. This helps from a manufacturing standpoint as well. While fewer cells are generated, the cells are more potent, so lower doses can still be effective for patients.
These have been very important learnings in the CAR-T field in recent years.
On the TIL side—tumor-infiltrating lymphocytes—the process is quite different. We start with a piece of tumor tissue, and we don’t know exactly how many T cells are present in that sample. Typically, it’s a relatively small number.
Expanding enough cells for therapy takes longer, and the doses required are much higher—up to 100 billion cells, which is a very large number.
So for TILs, the end-to-end process is longer, usually around 28 days, even with recent advances.
David Brühlmann [00:17:43]:
Yeah, let’s look at the TILs—tumor-infiltrating lymphocytes—and also the CAR-T cells in more detail. What are the main differences in how they work once they are injected into patients?
Chantale Bernatchez [00:17:58]:
So CAR-T cells are engineered from the patient’s blood—specifically circulating T cells. These T cells are modified to express a chimeric antigen receptor (CAR) that recognizes a specific target on the surface of tumor cells.
Essentially, we are repurposing T cells, which normally recognize pathogens and other foreign entities, to recognize a single target with high avidity. This is because the recognition mechanism is antibody-based—the antibody moiety fused to the surface of the T cells enables this specificity. When the CAR binds its target, it triggers full activation of the T cell and induces cytotoxicity, allowing the T cell to eliminate the target cell.
These are very potent cells, but they recognize only one antigen on the tumor surface. This means tumors can develop resistance by downregulating that specific antigen, thereby escaping CAR-T cell recognition.
As I mentioned, these engineered T cells are highly effective killers. This therapy has been particularly successful in hematologic malignancies, especially B cell–derived cancers, by targeting the CD19 surface antigen. CD19 is expressed on both normal and malignant B cells, so CAR-T cells eliminate both populations. However, this type of toxicity—loss of normal B cells—is clinically manageable, for example through immunoglobulin replacement therapy.
One of the major challenges with CAR-T therapy is identifying target antigens that are uniquely expressed on tumor cells and not on normal tissues. This is especially difficult in solid tumors, where such tumor-specific antigens are rare. While some solid tumor CAR-T trials have shown promise, overall success has been more limited due to tumor heterogeneity and lack of ideal targets.
On the other hand, T cells derived directly from tumor tissue—used in tumor-infiltrating lymphocyte (TIL) therapy—are not genetically engineered to change their antigen specificity. Instead, we rely on their naturally occurring antitumor reactivity.
The immune system has evolved to generate highly diverse T cell receptors (TCRs), educated in the thymus, that can recognize a wide range of foreign antigens—including tumor-associated antigens. Tumors often express mutated or overexpressed proteins that are not present in normal cells, making them visible to the immune system.
In many cases, tumors are eliminated early by immune surveillance. However, when tumors grow, it indicates that T cells have failed to fully eliminate them.
Dr. Steven Rosenberg at the NIH made a key observation: T cells that recognize tumors are often found in higher concentrations within the tumor itself. This is because T cells migrate toward their antigen. If they cannot eliminate the tumor, they accumulate there.
TIL therapy leverages this by harvesting these tumor-infiltrating lymphocytes, which are enriched for antitumor activity but suppressed by the tumor microenvironment. We take them ex vivo, remove them from that suppressive environment, and reinvigorate them through culture—providing nutrients, cytokines, and stimulation to restore their function. At the same time, we expand them to very large numbers.
The patient is then reinfused with billions of these reinvigorated T cells, which can return to the body and attack the tumor.
The first FDA-approved TIL therapy product, approved in 2024, is an unengineered product—meaning it consists of naturally occurring T cells expanded ex vivo and reinfused into the patient.
Next-generation products aim to further enhance these cells—for example by adding cytokine support or other functional improvements—without changing their antigen specificity. This is important because one of the strengths of TIL therapy is that it targets multiple antigens simultaneously, unlike CAR-T cells, which target only one.
As a result, it may be more difficult for tumors to develop resistance to TIL therapy, since it involves a broader, multi-antigen attack.
David Brühlmann [00:23:01]:
Yeah, that’s very powerful. So there’s no doubt that these are highly potent therapies. The challenge today is really in manufacturing.
You’re working on developing standardized processes, but once you have those, you also need to think about distribution. Before we move on to that, I’d like to ask one more question about process development.
You’re dealing with tremendous variability in starting materials, and you’re trying to, in a sense, “fit” them into a production platform. I imagine that’s not easy, and that you encounter biological limits along the way. What are some of the key challenges you face?
Chantale Bernatchez [00:23:49]:
Yes, we try to standardize our methodologies for both CAR-T and TIL therapies. Over the years, these standards have evolved, and we have identified more streamlined ways to grow these cells—but there is still room for improvement. For CAR-T, the product involves fewer cells compared to TIL therapy, and there are semi-automated systems that can support manufacturing in a more controlled way. However, we are not yet at a fully automated process. Many early-phase clinical studies still rely on manual culture methods, which remain the standard in cell therapy due to the complexity of the cells.
There is still significant operator involvement required. Moving forward, we need more automation and fewer manual steps.
With the success of cell therapies, there is growing interest from instrument developers in creating fully automated manufacturing systems. Some are currently being tested, but we’re not there yet.
One challenge in process development is that fully automated systems are very expensive. Early-stage programs—especially in academia or small biotech companies—often cannot afford them. So they start with manual processes, typically using flasks, and generate clinical proof of concept.
Once efficacy is demonstrated, there is a push to scale up and streamline manufacturing. However, transitioning to a new platform at that stage carries risk. Changing the manufacturing process can alter the properties of the cells in ways that are difficult to measure or predict.
This creates a high burden of comparability—developers must demonstrate that the new process produces a product with equivalent quality and clinical efficacy.
So it becomes a bit of a conundrum: it’s difficult to start with highly automated systems, but also risky to switch to them later.
Some newer approaches aim to automate individual manual steps—essentially robotizing parts of the process—to reduce operator involvement while maintaining consistency.
We’ll see how well these approaches work over time. I think they are promising.
In cell therapy, we often say that “the process is the product,” because the manufacturing process directly influences the properties of the cells. So if you change the process, you may change the product.
That’s why, ideally, it’s better to stay with the same manufacturing platform from early development through to commercialization.
David Brühlmann [00:26:53]:
It definitely makes sense, because the process will change the product you're producing.
Chantale Bernatchez [00:26:58]:
Yes. And we don’t yet fully know all the critical quality attributes that are important for clinical response. I think it’s also important to point out that even for CAR-T cells—which have a defined target that we can measure precisely, including how effectively they recognize and kill target cells—clinical efficacy is not necessarily correlated with how well they eradicate tumor cells in vitro.
There are other properties of the cells that matter. For example, we now understand that the “cargo” of the cells—that is, the type of T cells used to carry the CAR—plays an important role. Whether the cells are more differentiated or more naive-like can significantly impact clinical response.
So I think we still have a lot to learn about the characteristics of the cells we infuse that truly correlate with clinical outcomes.
David Brühlmann [00:27:46]:
We have covered the fundamental biology distinguishing tumor-infiltrating lymphocytes from CAR-T cells, and why translating these therapies from discovery into scalable, patient-ready treatments is unlike anything else in biologics manufacturing.
In part two, Chantale goes deeper into next-generation approaches, technology transfer, and what needs to change to broadly expand patient access.
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.
If you found value in today’s episode, take a moment to like, follow, and leave a review on Apple Podcasts or your favorite platform—it helps us reach and support more scientists like you.
Thanks for tuning in to the Smart Biotech Scientist podcast and being part of this journey toward bioprocess mastery. For more insights and practical tips, visit www.smartbiotechscientist.com.
About Chantale Bernatchez
Chantale Bernatchez is an immunologist and leading expert in T-cell therapies with over 20 years of experience spanning academia and biotechnology. She joined MD Anderson Cancer Center in 2007, where she specialized in tumor immunology and became deeply involved in adoptive T-cell therapy research. Chantale has overseen the GMP production of tumor-infiltrating lymphocyte (TIL) therapies, contributing to clinical studies in metastatic melanoma that demonstrated strong and durable patient responses. She later directed a research laboratory focused on improving TIL expansion and function.
Since 2020, she has transitioned into a biotech-focused role and now serves as Head of Process Development at CTMC, a joint venture between MD Anderson and National Resilience. In this role, she leads a multidisciplinary team advancing CAR T and TIL therapies through innovative process development and manufacturing strategies, holding multiple patents in adoptive cell therapy.
Connect with Chantale Bernatchez on LinkedIn.
If you’re interested in exploring further the concepts we touched on—such as cell therapy manufacturing, process control, and scaling living therapies—take a look at these related discussions:
Episodes 105 - 106: From Proteins to Cell Therapy: Why ATMPs Aren’t Just Complex Biologics with Oliver Kraemer
Episodes 109 - 110: Spinning Like Earth: Designing Low-Shear Bioreactors for Better Cell Culture with Olivier Detournay
Episodes 125 - 126: How to Enhance Cell Engineering Using Mechanical Intracellular Delivery with Armon Sharei
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
Getting an NDA signed shouldn’t feel like waiting for FDA approval. If your CRO needs weeks just to kickstart paperwork, your project’s clock is already ticking in the wrong direction.
In this episode of the Smart Biotech Scientist Podcast, David Brühlmann welcomes Dr. Ron Najafi of Emery Pharma for an in-depth conversation on CRO partnerships, analytical challenges in biologics, and his learnings from decades as an entrepreneur.
One of the things that I would say: if a CRO takes more than 48 hours to get an NDA signed, it's the wrong CRO, we're going to have a problem. If they say we're going to have six months before we get your project started, if they'll take two weeks to give you a proposal, one of the things I'm very proud of is we turn around NDAs in 48 hours, often less, and we promise to develop a proposal within 48 to 72 hours.
David Brühlmann [00:00:32]:
Welcome back. In Part One, Ron Najafi walked us through a career built on scientific rigor and entrepreneurial conviction. Now we go deeper into the practical questions that matter for your work: how to structure a productive CRO partnership, what separates a boutique specialist from a full-service organization, and what the future of pharmaceutical analytics actually looks like. Ron also shares the hard-won lessons that three decades of building companies tend to produce.
Besides the impurities now, as you're working with various clients, what other challenges do you see in the analytical field where you help your clients, as a CRO?
Ron Najafi [00:02:31]:
Some of the things that we've gotten involved in include doing pharmacokinetics. For example, for a small molecule, it's relatively straightforward. But if you're doing pharmacokinetics in the eye, in the vitreous humor—which is the liquid inside the eye, roughly about one or two milliliters—you need to develop a method, you need to be able to validate that method, and you need access to the vitreous.
So we've actually been involved in that kind of activity where we bought vitreous humor from an eye bank and got involved in doing that. We actually published that work with a company called Rayner Surgical. If you search Rayner Surgical and Emery Pharma, you'll find that we have significant expertise in ophthalmological pharmacokinetics. And that goes back to my experience with NovaBay as well, where we were involved in ophthalmology.
I have a lot of ophthalmological key opinion leaders—ophthalmologists near me who are friends of mine—and we've been getting calls from them wanting to work with us on different projects. As a result, we have a lot of experience there.
Another challenging area is assessing proteins. For example, if a gene therapy company is trying to silence a gene and therefore trying to see whether a certain protein level goes down or up in a very systematic fashion in an animal study, we've actually done that.
And these are not your average pharmacokinetics, David. You have to take the blood sample, get to the plasma, basically trypsinize everything. Before you do that, you have to know the sequence of your protein, perform an in silico analysis of what peptides would be generated as a result of trypsin digestion, identify several peptides, and then use those peptides as surrogate markers for the entire protein.
Then you synthesize the peptide, develop a reference standard of the peptide, and use it for a calibration curve. It's much more involved and much more sophisticated than small molecules. Biologics have their own challenges, and we have established a very extensive, thoughtful workflow—a systematic workflow that gives us what we want.
So if there is a small change in the gene therapy, we are able to detect it. Does it work? Does it not work? All of that is done through pharmacokinetic studies.
David Brühlmann [00:05:20]:
You work in different fields, you do impurity testing, you do pharmacokinetics testing, and a lot of other services. I'm just wondering, if Smart Biotech Scientist is looking for a CRO, what are some factors they need to consider to understand which CRO is a good fit for them? Because there are big ones, there are smaller ones, there are more diverse ones. What is the advice you're giving them?
Ron Najafi [00:05:51]:
Well, I would say you want to have a CRO that is big enough to have the resources, but small enough to move quickly. Some of the larger CROs are very bureaucratic. They have a lot of turnover. They have workflows, but they don't have the expertise to figure out, if something didn't work in that workflow, why it didn't work. They use technicians for the most part to conduct some of the activities.
One of the things that I would say is if a CRO takes more than 48 hours to get an NDA signed, it's the wrong CRO, we're going to have a problem. If they say we're going to have six months before we get your project started, if they'll take two weeks to give you a proposal, one of the things I'm very proud of is we turn around NDAs in 48 hours or less, often less, and we promise to develop a proposal, if we have all the key information we need, within 48 to 72 hours, we have a proposal to the client, and a very extensive proposal.
One of the things that our clients are very impressed with is how extensive our proposals are and how we lay things out. Of course, we also do more sophisticated activities.
We do peptide mapping and then we validate it. We do GLP-type activities, and that can be submitted to the agency, for example, for GLP toxicology studies if they need the data. Definitely, one of the things that I would always say is when you develop an analytical method, the method needs to be validated. Even if it's not GLP, you want to validate the method because there is a reason why the FDA requires validation. We want to make sure the method is robust. For example, if you get a frozen sample, have you tested the stability of your compound when frozen? Have you done that? They often have no idea. So you want to check the stability of your product in frozen conditions. Basically, do freeze–thaw stability, do room temperature stability. All of those things point to a good analytical method.
David Brühlmann [00:08:07]:
I would like to ask you about how do you develop a good relationship with your customers? Because when you're choosing a CRO, or the same applies for a CDMO, this is a relationship you establish that will go on sometimes for several years. So it's almost like a marriage. So how do you go about that?
Ron Najafi [00:08:29]:
I think managing expectations is the number one thing. I always tell my team to make sure if we have any challenges in analytical methodology, or if we're going to have delays, if an instrument breaks down or needs to be serviced, we maintain a close, honest relationship with our clients. We work as if we're part of their operation, part of their team.
From the moment the proposal gets signed and goes into our chemistry group, biochemistry group, or biomarker group, we assign a scientist who the client can reach out to, and that scientist updates the client sometimes once a week, sometimes twice a week. So we maintain a very close relationship, a very honest relationship, and we collaboratively try to solve problems. At the end of the day, we need to develop trust, and that trust comes from demonstrating our experience and expertise, utilizing that experience, and consistently managing expectations. That’s how trust is built.
David Brühlmann [00:09:37]:
Yeah, absolutely. But developing trust is key in any customer relationship or in any entrepreneurial endeavor. I would like to go back to your entrepreneurial story and just ask you: what were, I'd say, the number one or two main key lessons you have learned as an entrepreneur?
Ron Najafi [00:10:00]:
Well, I've had multiple chapters. If you asked me about fundraising from the days that I raised money for NovaBay, I raised $100 million from private and public investors, and I brought in $100 million in partnership revenue that came through NovaBay.
On that front, I will say: raise more money. When you have an opportunity to raise money, when money is offered to you, don't negotiate too hard on valuation and so forth. Take the money, because having that money will be more important than not having it. You don't know what's going to happen as you go through clinical studies—you could have hiccups, you could have challenges.
So number one: raise more money than you need.
Number two: make sure you bring on board good people—people who are creative, flexible, hardworking, and smart. In the absence of knowing where you're going, if you're lost, smart people are the best thing to have. If you're lost in a desert, it's better to be with a bunch of really smart, entrepreneurial, creative, positive-minded people.
So those are two things that come to mind from my experience at NovaBay.
The third thing I would say is: when you have an asset and you want to partner that asset, make sure you have the right people helping you with mergers and acquisitions and partnering. You need the right skill set to actually find those partnerships. It took us a while to get that, but that's really, really important because that creates a lot of value for you down the road—partnership opportunities.
Now in the next chapter, which is really different, I don’t have any investors at Emery Pharma. It goes back to the team. We have to make sure the team has the skill set, has the equipment they need, the right attitude, morale, and environment. All of that is really important. I think this is also true in any company—even in my previous chapter—but not having investors is a blessing in this chapter of my life because I don’t have to deal with investors or fundraising, and I can go to bed a lot easier.
David Brühlmann [00:12:24]:
That's good to hear. I imagine you have a lot more freedom and independence.
Ron Najafi [00:12:31]:
I tell you, one of the things I would say is: if you don’t have to take a company public, don’t take the company public. Having a public company and running a company is like having two different entities. You have to deal with the public company, you have to deal with investors, you have to deal with institutional investors, and then you also have to run the company. It’s a lot of work, a lot of challenges.
I enjoyed it. Would I do it again? I probably wouldn’t. I much prefer the private enterprise where you can focus on the company, its goals, and its success.
David Brühlmann [00:13:06]:
I'd just like to look ahead a few years and I'm curious about your vision of how you see biologics evolving. According to you, Ron, what are the biggest unmet analytical challenges in biologics?
Ron Najafi [00:13:23]:
Let me tell you—this actually came up in a recent meeting we had with a government entity that is trying to develop a product for government use. One of the biggest costs in developing biologics is bioanalytical testing. For this product, the analytical portion was several million dollars.
The reason is they used old-fashioned ELISA techniques. They had five or six different biologics, and they needed to measure all of them using ELISA. Each ELISA needs to be validated, and each requires separate testing. Mass spectrometry cuts through a lot of that.
We think we can reduce the cost of the bioanalytical portion of biologics by probably 50%. That’s going to be a major breakthrough for biologics development.
Now, for personalized medicine, that’s still a tall order. We still need to develop methodologies for testing and qualifying and controlling some of those biologics. But for larger products that are commercialized through traditional pathways, I think bioanalytical testing costs can come down significantly.
David Brühlmann [00:14:48]:
Wow. And this is just because the technology will evolve and we will develop better workflows?
Ron Najafi [00:14:54]:
Absolutely. The technology is already here. We’ve actually been doing this at Emery Pharma. We’ve shown various government partners that this can be done and that costs can be lowered. So I say that with confidence.
We can test a mixture of six antibodies in blood in one run using mass spectrometry—one run versus six different runs, six different validations, and so on. We can perform one validation, one run, and obtain data that is more accurate than traditional ELISA techniques.
David Brühlmann [00:15:30]:
Well, Ron, we have covered a lot of ground today. What is the most important takeaway you want our listeners to walk away with?
Ron Najafi [00:15:40]:
There is a lot of excitement and enthusiasm in biopharma, biotech, and biologics. There are many peptides being developed—whether GLP-based or other types of peptides—for a wide range of indications.
We have a broad range of clients developing peptides for many applications, and I think the speed of development can be improved. I still believe people need a better understanding of how the FDA operates—how to interact with the FDA effectively. That experience and expertise is important.
Overall, I’m very enthusiastic about the future. If your listeners would like to reach out, they can connect with me via LinkedIn. My email is ron@emerypharma.com. Having my email helps ensure successful connection on LinkedIn. We also have a YouTube channel, Emery Pharma, where we host a speaker series similar to one I previously created. It features guests, including yourself, and other experts.
One more thing, David: I have my digital twin available on our website. Essentially, it’s a digital twin of me, and you can ask it any questions about what we do and how we do it. We’ve spent significant time training it using the content on our website so it can provide detailed information.
David Brühlmann [00:17:30]:
Excellent, Ron. There you have it. Smart Biotech Scientist listeners, you can use Ron’s digital twin—but also feel free to reach out to him and his team. You’ll find the links in the show notes. Ron, thank you very much for being on the show today. It has been a huge pleasure.
Ron Najafi [00:17:49]:
Thank you, David. The pleasure is all mine, and I look forward to working with you.
David Brühlmann [00:17:54]:
Nitrosamines, CRO selection, and the future of analytics—Ron covered it with the precision you’d expect from someone who has lived these challenges firsthand. If you work in drug development, there is something here worth reflecting on.
And if today’s episode was useful to you, please leave a review on Apple Podcasts or your platform of choice. Thank you for tuning in today, and I’ll see you next time.
All right, smart scientists, that’s all for today on the Smart Biotech Scientist Podcast. Thank you for tuning in and joining us on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. For additional bioprocessing tips, visit us at smartbiotechscientist.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let’s continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Ron Najafi
Dr. Ramin (Ron) Najafi is a veteran pharmaceutical scientist and entrepreneur known for bridging innovation, safety, and regulatory science. As the Founder of CP Lab Safety, he has championed safer laboratory environments and sustainable practices across the industry.
He is also the Founder and CEO of Emery Pharma, a science-driven CRO recognized for its deep expertise in CMC and advanced bioanalytical testing. Through Emery Pharma, Dr. Najafi and his team support PK, PD, and TK studies with a strong commitment to scientific rigor, patient safety, and regulatory excellence.
Connect with Ron Najafi on LinkedIn.
If you’re interested in this topic, check out these episodes on building a robust scale-up strategy. To get it right, you need to view the process from multiple angles—regulatory, digital, and operational.
Episodes 23 - 24: Strategies for Success: Master CMC Development with Gene Lee
Episodes 57 - 58: Crafting a Solid CMC Strategy: Key Factors and Common Pitfalls with Matthias Müllner
Episodes 139 - 140: Regulatory Secrets Revealed: Why Your CMC Strategy Could Make or Break Your Biotech Startup with Rivka Zaibel
Episodes 189 - 190: Why Smart Biotech Founders Plan CMC First (While Competitors Burn Cash Later)
Episodes 199 - 200: Mastering Quality by Design: From Product Failures to Commercial Success in Biologics CMC Development
Episodes 203 - 204: Mastering CRO Selection: Essential Questions for CMC Analytical Development with Daniel Galbraith
Episodes 231 - 232: From IND to BLA: The Biologics CMC Decisions That Determine Regulatory Success with Henri Kornmann
Below, you’ll find a curated collection of resources, technical guides, and regulatory links shared by our guest.
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
