Is your manufacturing strategy bleeding money in ways you can’t see? The production floor is full of hidden cost traps—capital investments, labor, resin lifetime, and facility flexibility—that often dictate the business fate of biologics and biosimilars.

This episode of the Smart Biotech Scientist Podcast turns the spotlight on process economic modeling: the tool that’s reshaping how manufacturers understand and control cost drivers behind monoclonal antibody and biologics production.

In this episode from the Smart Biotech Scientist Podcast, David Brühlmann meets Niklas Jungnelius, Process Modeling Leader at Cytiva, a global biotechnology leader dedicated to helping customers discover and commercialize the next generation of therapeutics.

Key Topics Discussed

• Whether fully continuous biomanufacturing will dominate mammalian systems in the next 15 years.
• How production scale affects cost per gram or dose.
• Advantages, disadvantages, and scalability considerations for each technology.
• How modeling influences strategic decisions in biotech manufacturing.
• How production costs impact pricing and competition between biosimilars and innovative drugs.
• Balancing flexibility, capital investment, and operating costs when adopting single-use systems.
• Situations where traditional batch processes remain advantageous.
• The impact of resin costs, especially at clinical scale, and other often-overlooked economic factors in facility planning and budgeting.

Episode Highlights

• What process economic modeling is and how it differs from mechanistic modeling [06:40]
• Main cost drivers in biologics and biosimilars manufacturing, and their direct and indirect impacts on patient pricing [09:21]
• The role of manufacturing scale and productivity in driving down costs, and how facility type (stainless steel vs single-use) affects labor, flexibility, and investment [11:51]
• Trade-offs between single-use and stainless steel facilities at intermediate production scales [15:07]
• Key differences in cost and efficiency between fed-batch and fully continuous manufacturing, including productivity limitations and capital/expenditure implications [16:20]
• The impact of resin lifetime and under-utilization on clinical manufacturing costs [19:51]
• Hidden or less quantifiable costs such as facility flexibility, excess capacity, safety margins, and the importance of realistic assumptions in economic modeling [20:20]

In Their Words

The key cost drivers — two of them would be the scale of manufacturing and process productivity. The production volume is critical, as we see a very strong benefit from economies of scale when looking at the manufacturing cost per gram of product or per dose produced.

With a larger facility and larger bioreactors, you can reduce factors such as labor cost and capital cost investment per gram of product produced.

Similarly, if you have higher productivity in bioreactors — meaning higher titers — that’s also very helpful in reducing overall cost.

Episode Transcript: Process Economics Decoded: How to Model Biomanufacturing Costs From Clinical to Commercial Scale - Part 1

David Brühlmann [00:00:41]:
Ever wonder if that expensive new technology will actually save you money in the long run — or which process parameters are secretly eating your budget alive? Today, we're diving into the world of process modeling with Niklas Jungnelius, who is a Process Modeling Leader at Cytiva.

With over 25 years in the life sciences industry, Niklas helps biotech companies make smarter decisions about manufacturing economics. Whether you're comparing fed-batch vs. perfusion or trying to justify a capital investment, this episode will change how you think about process costs. Let's jump in. Niklas, welcome — it’s great to have you on today.

Niklas Jungnelius [00:02:37]:
Thank you so much, David. Glad to be here.

David Brühlmann [00:02:40]:
Niklas, share something you believe about bioprocess development that most people might disagree with.

Niklas Jungnelius [00:02:47]:
Sure, David. I would say that I personally do not believe that fully continuous bioprocessing will become the dominant manufacturing mode for new mammalian cell culture processes within the next 15 years.

That may go against what many people in the industry expect, but the main reasons I think this way are as follows:

• First, fully continuous processes introduce significant complexity — both in process development and day-to-day operations. This added complexity can increase the risk of costly delays to market, and it can also limit technology transfer options to CMOs (Contract Manufacturing Organizations) if outsourcing is part of your strategy.

• Second, there is already a large global base of stainless-steel manufacturing capacity designed for batch-mode operations, and those facilities are not easily converted to continuous operation. As a result, I think we will continue to see ample, cost-competitive capacity available for batch manufacturing for the foreseeable future.
• Finally, I would say the business case for fully continuous manufacturing is still not entirely clear. While you can achieve higher productivity in upstream operations, you often do not gain the same efficiency downstream, which can limit the overall benefit.

For these reasons, I believe that intensified fed-batch processes — offering many of the performance advantages of continuous without the same level of complexity — will remain a very attractive and practical option for most companies moving forward.

David Brühlmann [00:04:02]:
I’m curious, Niklas — what originally drew you into life sciences, and ultimately to process modeling? What were some interesting steps along your career path?

Niklas Jungnelius [00:04:15]:
Yeah, I think it really started back in high school. During my final year, we had a new biology and biochemistry teacher — a man named Anders — who had previously worked at a university doing both research and teaching. He told us all these fascinating things about new methodologies and tools emerging within biopharma and life sciences, and that really caught my attention.

Originally, I had planned to study computer science, but I changed my mind and instead applied to university for chemical engineering and biochemistry. After completing my degree, I explored a few different areas — I worked in marketing, customer service and support, and commercial operations. But about 14 years ago, I applied for a position within what was then GE Healthcare’s Strategic Marketing organization. I was hired and immediately felt at home, initially working with portfolio strategies for chromatography products.

Over time, I expanded my scope to include all types of downstream operations, and as part of that, I became involved in process modeling. I found it fascinating to understand how different technological and operational choices affect performance and economics, and to assess the value and impact of those choices. That analytical aspect really intrigued me, and I wanted to work more closely with it — and also more closely with our end users.

When the company transitioned to Cytiva, I was asked to lead our work in process economic modeling. That sounded very appealing, and in that role, I now collaborate with both internal stakeholders — helping assess the impact and benefits of new technologies, supporting product portfolio roadmapping and technology evaluations — and with external customers, helping them make the best process technology choices for their specific needs. That’s what I do today.

David Brühlmann [00:06:23]:
For those listening who might be new to process modeling, can you explain what it actually involves? What does it do, what’s the purpose, and what are you essentially building when you create these models?

Niklas Jungnelius [00:06:40]:
Yes, absolutely. First of all, I like to clarify that what I do is process economic modeling, not mechanistic modeling. Mechanistic modeling focuses on simulating physicochemical properties — for example, how a chromatography separation behaves under different conditions. In contrast, process economic modeling looks at the manufacturing process from an economic and capacity perspective — things like cost structure, equipment utilization, and increasingly, environmental sustainability.

In these models, we account for all key variables that influence process efficiency and output. The questions can be quite diverse. Sometimes we focus on individual unit operations, which we might model in Excel — that gives us full control over the parameters, calculations, and outputs.

Other times, we model entire manufacturing processes end-to-end. For that, we typically use third-party software, most often BioSolve (by Biopharm Services). BioSolve includes a lot of predefined parameters — which can be customized — and allows us to evaluate both the performance of individual steps and the downstream impact when one operation changes.

In all cases, these models rely on mass balance calculations — we track how much product flows through each step, the duration of each step, and from that, determine equipment sizing, consumable requirements, labor needs, and so on.

We also include assumptions about labor costs, capital costs, and facility investments, which together allow us to estimate the total cost of goods (CoG) with reasonable precision. When modeling environmental sustainability, we extend this by analyzing consumable usage, material composition, and facility requirements — such as cleanroom areas and air handling needs. For instance, larger cleanrooms require more air changes per hour, which in turn drives energy consumption. So all these factors come together to give a holistic view of both economic and environmental performance.

David Brühlmann [00:09:21]:
For example, the cost of manufacturing — or more precisely, the cost of goods (CoG) — has gained a lot of attention in recent years, especially as cost pressures on biologics have increased.

Before diving into the details, can you give us a high-level picture of the main cost drivers and how they influence biologic production — or even the price patients ultimately pay for life-saving therapies?

Niklas Jungnelius [00:09:55]:
Yeah, that’s a good question. To answer it properly, I need to take a step back and not just look at modeling the manufacturing cost, because as you know, there are many other costs involved in biopharmaceutical development.

For innovator molecules, the price per dose is not primarily determined by the manufacturing cost — the margins are typically high. That’s because companies need to recover the significant investments made in R&D, clinical trials, regulatory approvals, and commercialization. So traditionally, in the case of innovative biologics, the main cost pain point for biopharma companies has not been at the manufacturing stage.

However, we are now transitioning into a market with an increasing number of biosimilars being introduced. And perhaps I’m a bit mAb-focused here — since monoclonal antibodies are where we do most of our work — but in the biosimilar segment, the landscape is much more competitive. Margins are lower, and the cost to bring these products to market is significantly less than for new, innovative therapies.

As a result, manufacturing cost becomes a much more critical factor for biosimilar producers. Being able to reduce production costs directly improves competitiveness against other biosimilar manufacturers.

That said, whether we’re talking about innovator biologics or biosimilars, the top priority remains having a robust, high-quality process with sufficient production capacity to ensure uninterrupted product supply. Any supply interruption or manufacturing downtime can be extremely costly and damaging to both the business and patients.

David Brühlmann [00:11:33]:
From your modeling work, Niklas — especially as you mentioned for mAbs (monoclonal antibodies) — what parameters have the biggest impact on mAb production costs? And have you found any surprising insights there? Let’s start with fed-batch processes, and then we can move on to other process formats.

Niklas Jungnelius [00:11:51]:
I’d say the key cost drivers — two of the most important — are manufacturing scale and bioreactor productivity. The production volume has a huge impact because we see very strong economies of scale when we look at the manufacturing cost per gram of product or per dose. With a larger facility and larger bioreactors, you can reduce costs like labor and capital investment per gram of product produced.

Similarly, if you achieve higher productivity in your bioreactors — meaning higher titers — that’s extremely helpful in lowering overall costs. This is true for fed-batch manufacturing, but it’s even more critical for perfusion processes, where media consumption represents a much larger portion of the total cost. If you can make that operation more efficient — for example, producing more product per gram of media consumed — it significantly improves the process economics.

We should also mention the difference between stainless steel and single-use facilities. In stainless steel setups, you have substantial capital investment costs, as well as higher labor costs due to all the cleaning and line clearance activities required.

In contrast, single-use processes require significant spending on plastic consumables — such as bags, tubing, and connectors — but they bring major advantages. They reduce labor needs, shorten turnaround times between batches, and increase facility throughput, which helps improve overall productivity and agility.

David Brühlmann [00:13:33]:
Right, and I imagine that while cost is one driver, single-use systems also give you a lot of flexibility. So it’s always a trade-off between running and maintenance costs versus the flexibility you gain — and also the scale at which you operate.

Niklas Jungnelius [00:13:52]:
Yes, definitely. Over the past couple of decades, we’ve seen a major shift toward single-use technologies precisely because of that increased flexibility.

As you said, you have lower facility construction costs, shorter lead times to get capacity online, and generally more agile operations. At the same time, I’d like to point out that we still see many large stainless steel facilities being built — especially in regions like Korea, the U.S., and Europe — where companies are investing in 10,000-liter or larger stainless steel bioreactors. That’s because the economy of scale at that size is so favorable; for very high-volume products, large stainless steel production can be extremely competitive.

David Brühlmann [00:14:46]:
What about the intermediate scale — say, up to 2,000 liters — where you could use either stainless steel or single-use systems? Are there situations where one is clearly preferable, or is it more of a case-by-case decision?

Niklas Jungnelius [00:15:07]:
It’s very much case by case, but generally speaking, the smaller the scale, the more advantageous single-use becomes.

That’s because stainless steel facilities involve very large fixed investments, almost regardless of scale. And just to clarify, I’m mainly referring here to mammalian cell culture capacity — the picture might look quite different for microbial fermentation.

The flexibility of single-use systems has tremendous value, even if it’s not always easy to quantify. Having the freedom to make late-stage decisions or bring new capacity online faster can reduce the safety margins you need in your supply chain. That agility can make a big economic difference over time.

David Brühlmann [00:15:48]:
Now let’s talk about continuous manufacturing. Even though you’ve expressed some reservations — that not everyone will adopt it because of the added complexity — it does offer economic advantages, especially when moving toward intensified or even end-to-end continuous processes.

From a purely economic standpoint, what are the main differences between these process types? What are the advantages and drawbacks?

Niklas Jungnelius [00:16:20]:
If we start with fed-batch, in the classical setup, your bioreactor productivity isn’t that high because much of the run time is spent on cell growth — expanding the cells to a suitable density before significant product formation begins.

Once you reach downstream processing, you typically have large batch volumes that you can process efficiently — so your downstream utilization is high. However, if you only have a single bioreactor feeding downstream, you’ll still have idle time between runs, which lowers your overall facility utilization and increases your capital cost per gram.

In a perfusion or continuous process, your bioreactor operates at high productivity, delivering product continuously to downstream. But here, the downstream line becomes the limiting factor — it can only operate at the same throughput as upstream. You don’t gain much benefit from trying to speed up individual steps, because everything is connected in real time.

For example, in a continuous mAb process, you might have an upstream productivity of 1 g/L/day, feeding into the capture step. After Protein A capture, you concentrate the product — say, from 1 g/L to 10–20 g/L — which reduces the downstream process volume dramatically. So, if your bioreactor produces 1,000 liters per day, that might translate to only about 2 liters per hour through the later purification steps. It’s a very slow, “dripping” flow, which isn’t the most efficient way to run those unit operations.

You could add multiple perfusion bioreactors to the same downstream line to improve utilization, but that brings its own technical and operational challenges. So, in summary, the key question is: Where do you want your productivity gains? In fed-batch, you can keep your downstream line fully utilized by staggering multiple bioreactors. In continuous, you gain upstream efficiency but lose some flexibility in downstream.

One additional point — in clinical or low-frequency manufacturing, resin cost becomes a major factor. Chromatography resins are a sort of hybrid cost item — part consumable, part capital investment — with long lifetimes, often up to 100–200 cycles.

If you only run a few batches and can’t fully use that lifetime, the effective resin cost per batch becomes very high. In continuous or perfusion processes, you can use smaller columns and cycle them repeatedly over longer runs, which reduces the total resin requirement and spreads out the cost more efficiently. So resin cost tends to be a major driver in fed-batch processes, but less so in continuous ones.

David Brühlmann [00:20:04]:
That’s a really good point about resin lifetime, especially for clinical-scale manufacturing. Beyond that high resin cost, are there other hidden costs that companies might overlook before doing proper process economic modeling?

Niklas Jungnelius [00:20:20]:
Yes, absolutely — there are several. Some are hard to capture directly in a model, like risk of process failure or the value of operational flexibility. We can include proxy parameters for those, but there’s always a subjective element.

Another common oversight is the cost of underutilized capacity. When launching a new product, companies often build facilities designed to meet future demand projections — say, five years down the line. But if actual sales don’t reach those forecasts, you end up with idle capacity, which is expensive unless you can repurpose it for other products.

This is where flexible, single-use facilities really shine — with shorter build times and modular capacity, you can align production more closely to real demand, avoiding excessive safety margins on capacity.

Also, from a modeling perspective, it’s easy to over-optimize. As a process modeler, I often work in an idealized world — assuming everything performs exactly as expected. But in reality, you need to build in safety margins to account for process variability and unforeseen issues.

So my advice is: make realistic assumptions, don’t over-optimize, and always include reasonable safety factors in your calculations. That gives you a more credible and practical cost estimate.

David Brühlmann [00:22:32]:
Thank you for tuning in. Today we covered the fundamentals of process modeling and the critical cost drivers that can make or break your manufacturing strategy. In Part Two, we’ll dive into sustainability modeling and emerging technologies that are reshaping bioprocess economics.

If you found value in this conversation, please leave us a review on Apple Podcasts or your preferred platform — it helps other biotech professionals discover these insights.

Alright, smart scientists — that’s all for today on the Smart Biotech Scientist Podcast. Thanks for joining us on your journey toward bioprocess mastery. If you enjoyed this episode, please leave a review and visit smartbiotechscientist.com for additional resources and tips. Stay tuned for more inspiring biotech insights in our next episode — and until then, let’s continue to smarten up biotech.

Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.

Next Step

Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call

About Niklas Jungnelius 

Niklas Jungnelius is the Process Modeling Leader at Cytiva, where he advises drug manufacturers, industry associations, and internal teams on the implications of different process technology choices. He helps organizations make informed decisions to achieve goals in process economy, productivity, and environmental sustainability.

Niklas holds a master’s degree in Chemical Engineering from Chalmers University of Technology and brings over 25 years of experience in the life sciences industry—half of which has been spent in strategic roles at Cytiva and GE Healthcare.

Connect with Niklas Jungnelius on LinkedIn.

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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