For decades, biotechnology development relied on 2D cultures—cells grown as flat layers in petri dishes or flasks. While useful for many experiments, these models don't reflect the true complexity of living tissues.

In this episode of the Smart Biotech Scientist Podcast, host David Brühlmann speaks with Catarina Brito, Principal Investigator at ITQB NOVA and Head of the Advanced Cell Models Laboratory at iBET and ITQB NOVA in Portugal, about how 3D cell models are reshaping preclinical research and driving a fundamental shift in the field.

Key Topics Discussed

• The immune microenvironment critically influences therapeutic success, explaining resistance to immunotherapies and unexpected immune reactions to gene therapies.
• Advanced 3D tumor and neural models reveal immune responses and resistance mechanisms that are missed by traditional preclinical systems.
• Case studies show fibroblast-driven resistance in breast cancer and intense, transient innate immune responses to gene therapy vectors.
• Insights from advanced cell models align more closely with patient data, improving translational relevance.
• The future of cell models lies in integrating multi-omics, spatial data, AI, and predictive digital twins for precision medicine.
• Effective implementation requires question-driven, modular model design, starting simple and adding complexity only when biologically justified.
• Industry collaboration ensures models are predictive, scalable, and compatible with real-world drug development workflows.
• Advanced cell models are a cornerstone of next-generation translational research, supported by collaborative ecosystems like iBET and NOVA University.

Episode Highlights

• Understanding the contribution of stromal and immune cells to therapy outcomes in tumor microenvironments [03:42]
• Studying immune responses to gene therapy vectors with advanced neural models [04:31]
• Combining multi-omics and spatial data with AI for predictive biology and patient-specific digital twins [05:16]
• Catarina’s advice: Start simple, let the biological question dictate model design, and avoid premature overengineering [06:53]
• Importance of reproducibility, process controls, and standardization in advanced models [08:10]
• How academic-industry collaborations drive model development, scalability, and real-world relevance [08:42]
• Common pitfalls: Overengineering, poor cell source selection, insufficient system validation [11:03]
• Next steps for precision medicine and translational research using advanced cell models [13:08]

In Their Words

We have a breast cancer microenvironment model in which we include breast cancer cells alongside immune cell populations. We also incorporate stromal cells, namely fibroblasts, and we were studying antibody–drug conjugates (ADCs). These ADCs are very potent and can kill cancer cells quite quickly.

However, when we use a 3D model in which we reconstitute the tumor microenvironment, the response changes. The stromal cells—these fibroblasts—contribute to resistance to ADCs, and we can observe and study this effect directly in the 3D model. This is something you cannot—and would not—see in a flat, 2D culture flask.

Episode Transcript: From 2D Cultures to Advanced 3D Cell Models for Preclinical Research - Part 2

David Brühlmann [00:00:46]:
Welcome back. In part one, Catarina Brito introduced us to the power of advanced 3D cell models that mirror the complexity of human tissues farbeyond traditional culture systems.

Now we’re going deeper into the immune microenvironment, the hidden factors that can make or break therapeutic success. From immunotherapy resistance in tumors to unexpected responses to gene therapy vectors, Catarina reveals how understanding innate immunity is reshaping preclinical strategy. Plus, she’ll share her vision for the future. Let’s continue.

Can you give us a concrete example of how modeling the tumor microenvironment in your system has revealed insights about specific interactions or mechanisms? How does that work?

Catarina Brito [00:02:53]:
There are different types of examples. One practical example comes from work we have published, where we developed a breast cancer microenvironment model that includes breast cancer cells, immune cell populations, and stromal cells, namely fibroblasts. In this study, we were evaluating antibody–drug conjugates (ADCs).

Typically, in 2D models, we culture HER2-positive breast cancer cells and test an anti-HER2 ADC. These ADCs are very potent and can kill cancer cells quite quickly. However, when we move to a 3D model, in which we reconstitute the tumor microenvironment, the response changes. The stromal cells—specifically fibroblasts—contribute to resistance to these ADCs. This effect can be observed and studied in the 3D model, and it is something you cannot—and would not—see in a flat 2D culture flask.

Another example comes from our work with neural models, which we use to study the early steps of immunogenicity to gene therapy vectors. In these models, we include neurons, glial cells, and microglia, which are the resident macrophages of the brain. Using this system, we were able to identify very transient but intense immune responses to viral vectors. These responses had not been detected in other preclinical models but were observed in patients. This illustrates how better reproducing human physiology can help identify factors that can later be used to improve therapies.

David Brühlmann [00:04:44]:
Where do you see these models evolving in the next few years? There’s a lot happening in the bioprocessing field—how we culture cells, how we maintain conditions, advances in analytical technologies, and now the integration of AI and machine learning. How will these technological advances affect the way you conduct your experiments?

Catarina Brito [00:05:16]:
I think advanced cell models combined with multi-omics data—multiple layers of omics information—and spatial information will be key. This is particularly relevant when we consider three-dimensionality and the multiple components of these environments.

This type of data will feed AI-based models, allowing a more accurate representation of human biology and pathophysiology. Until now, we have mainly been making observations and comparing them with human data. But to start making predictions, the integration with AI will be essential.

One starting point is thinking about digital twins of the models themselves, benchmarked against patient-derived data. From there, many things become possible: better tailoring treatments to biological patient profiles, optimizing dosing strategies, and designing drug combinations. Overall, this will be a major step forward for precision medicine and its meaningful implementation.

David Brühlmann [00:06:28]:
What is your advice to a smart scientist listening and wondering how they could implement these advanced models? We have many powerful tools available, but it can also feel overwhelming. Where do you start, and how simple can you keep these models while still gaining value from them?

Catarina Brito [00:06:53]:
I think the answer lies in focusing on the biological question you need to answer. I completely agree with what you suggested—we should aim to be as simple as possible to answer that question. The technology should not be the driving force; the question should lead us to define the biological endpoint, whether that is mechanism of action, potency, or safety.

From there, we decide on the level of complexity required. One risk today is overengineering systems too early, before fully understanding the cells and the question. That’s why I advocate for modular systems: starting simply with tumor or organ-specific spheroids, and then adding stromal or immune components only when the project demands it.

At the same time, we should avoid oversimplifying by treating these systems like regular 2D cultures. Factors such as mass transfer, cell source selection, and process control are critical. These are processes, and they require proper control to ensure reproducibility and standardization, which is often what feels daunting with these models.

David Brühlmann [00:08:17]:
At iBET, you collaborate extensively with pharmaceutical companies. I imagine you also guide them in how to use the models effectively. Can you describe the types of collaborations you have, how you run them, and how they shape model design and application?

Catarina Brito [00:08:42]:
That’s true—we collaborate extensively with industry. Over the years, we’ve been fortunate to work with several pharmaceutical partners who have helped shape how we operate. Pharma partners bring questions driven by translational needs: how to make models more predictive, more scalable, and more compatible with their development workflows.

These applied questions strongly influence model design and help us prioritize our research directions. They help ground the models in real-world use. A key factor for successful collaboration is alignment and transparency—being very clear about the biological question, the performance metrics, and the limitations of the system.

Open data sharing in both directions has been essential. This approach has helped move several of our platforms from proof-of-concept to tools that genuinely contribute to accelerating translation within companies.

David Brühlmann [00:09:49]:
What kind of companies do you generally work with?

Catarina Brito [00:09:53]:
I’ve worked mainly with pharmaceutical companies. Among our collaborations, we’ve worked with Boehringer Ingelheim, Merck Global Health—which is the social business of Merck KGaA, Germany—and AbbVie in the United States. These are large companies developing advanced and novel therapies.

David Brühlmann [00:10:14]:
Do you also work with smaller companies or startups?

Catarina Brito [00:10:18]:
Yes, we do. The collaborations I mentioned were long-term partnerships, often spanning several years, where we developed models from scratch to answer questions that were not addressable with existing systems. We’ve also worked with smaller companies and startups, typically on more focused questions related to their compounds, using models we already have established.

David Brühlmann [00:10:44]:
I have a question about potential pitfalls. As we mentioned earlier, there are many technologies available—so where do you start? What do you leverage? What is hype? What really makes a difference? Can you point out two or three pitfalls to watch out for?

Catarina Brito [00:11:03]:
As I mentioned earlier, one major pitfall is overengineering systems in a technology-driven way before truly understanding the cellular system, what you need the cells to do, and which cell types are actually required.

Another critical aspect is the cell source. Depending on your question, you may never get the answer you’re looking for from the cells you’re using—no matter how much you engineer them.

A third pitfall is insufficient validation and characterization. Moving too quickly to application without properly characterizing the system can be problematic. Thorough validation is essential before drawing conclusions or translating results.

David Brühlmann [00:11:45]:
Catarina, what burning question haven’t I asked that you’re eager to share with our biotech community?

Catarina Brito [00:11:52]:
I think we covered things quite thoroughly. It was a really good conversation—and you can probably tell how passionate I am about this topic.

David Brühlmann [00:12:05]:
Yes, absolutely.

Catarina Brito [00:12:07]:
And this also gives context to my work. My academic appointment is with NOVA University Lisbon, but my lab is also affiliated with iBET. It is through iBET that I collaborate with industry, because iBET is a partnering research organization that bridges engineering and biology to accelerate biopharmaceutical development.

This creates an ecosystem where we combine expertise ranging from cell line development, cell bioprocessing, and advanced analytics, and then close the loop with translational models under the same organizational framework. This setup gives us access to a highly collaborative environment and makes it easier to work with advanced therapeutics, from antibodies to gene and cell therapies.

David Brühlmann [00:12:59]:
This has been great. Catarina, what is the most important takeaway you want our listeners to walk away with from our conversation?

Catarina Brito [00:13:08]:
I think the key takeaway is that advanced cell models are here—and they are here to stay. They will play a major role in shaping translational research in the future, while at the same time driving transformation across the entire industry.

David Brühlmann [00:13:24]:
This was great. Catarina, thank you so much for letting us into the world of 3D cell models. Where can people get in touch with you?

Catarina Brito [00:13:33]:
I’m on LinkedIn, like most people, and also reachable through the iBET website and ITQB NOVA’s website. But I would say LinkedIn is the best way to contact me.

David Brühlmann [00:13:42]:
Excellent, smart biotech scientists—you’ll find all the links in the show notes. Please take this opportunity to reach out to Catarina. And Catarina, once again, thank you so much for being on the show today.

Catarina Brito [00:13:56]:
Thank you, David. It’s been a pleasure. Bye-bye.

David Brühlmann [00:13:59]:
That wraps up our conversation with Catarina Brito. Her work exemplifies the future of preclinical research, where complexity becomes predictability and where patient-specific insights move from aspiration to reality.

If this episode sparked new ideas for your own development programs, I’d love to hear about it. And please take a moment to leave a review on Apple Podcasts or wherever you listen—your support helps us bring more innovators like Catarina to the biotech community.

Thank you so much for tuning in today. Until next time.

For additional bioprocessing insights, visit us at www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech conversations in our next episode. Until then—let’s continue to smarten up biotech.

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

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 Catarina Brito

Catarina Brito is Principal Investigator of the Advanced Cell Models Laboratory within the Animal Cell Technology Unit at iBET and ITQB-NOVA. Her research centers on the development of human cell models using induced pluripotent stem cells, patient-derived cells, and established cell lines, and on applying these models to investigate disease-associated alterations of the cellular microenvironment and their impact on therapeutic response. 

Her key research interests include the innate immune microenvironment in cancer and neurological disorders, as well as gene and cell therapies and immunotherapies. Her laboratory is supported by funding from FCT (Portugal), the Innovative Medicines Initiative Joint Undertaking (IMI; EU & EFPIA), and the international pharmaceutical industry.

Connect with Catarina Brito 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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