What happens when we move beyond oversimplified cell cultures and truly embrace the complexity of human biology? In this episode of the Smart Biotech Scientist Podcast, we explore how advanced 3D cell models are reshaping preclinical research—recreating human tissue microenvironments to better understand tumors, immunotherapies, and gene and cell therapies.

David’s guest is Catarina Brito, Principal Investigator at ITQB NOVA and Head of the Advanced Cell Models Laboratory at iBET and ITQB NOVA (Portugal). Her work bridges academia and industry through iBET, a unique partnering organization that integrates cell engineering, bioprocessing, and translational modeling.

Catarina’s pioneering models help both pharma leaders and startups predict drug resistance and immunogenicity earlier and more reliably, accelerating the path to safer, more effective therapies—well before clinical trials begin.

Topics discussed:

• 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)

Want to know how leading scientists make advanced cell models actionable and collaborative for pharma breakthroughs? Tune in for practical strategies, real-world collaborations, and pitfalls to avoid as you scale your own translational research.

Connect with Catarina Brito:

LinkedIn: www.linkedin.com/in/catarina-brito-ibet

Advanced Cell Models Lab – iBET: www.ibet.pt/laboratories/advanced-cell-models-lab

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What if the failure rate in clinical trials isn't about picking the wrong drug candidates—but about testing them in the wrong models?

When you move cells from a 2D culture plate into a bioreactor, you're not simply scaling volume. You're fundamentally changing the biological context. Cell density shifts. Mass transfer dynamics evolve. Mechanical cues emerge. The cells sense these changes and respond—often in ways that derail strategies built on oversimplified assumptions.

Most preclinical research still relies on flat plastic surfaces and animal models that miss critical aspects of human biology. The result? Therapeutics fail late in development because the models couldn't predict how human tissues would actually respond.

In this episode, 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. Catarina's career-defining insight came early: studying glycan-protein interactions in murine versus human cells revealed that species differences weren't just nuances—they were fundamental gaps that could mislead entire research programs.

Catarina and her team have developed neural, liver, and tumor models that capture the multicellular complexity and microenvironmental cues that 2D cultures cannot replicate. Her work creates preclinical models sophisticated enough to predict human responses while remaining scalable for drug development workflows.

Highlights of the episode:

• Limitations of traditional 2D cell cultures and animal models in capturing realistic tissue behavior and therapeutic responses (06:27)
• Catarina Brito's personal scientific journey: from discovering model limitations to pioneering 3D culture systems in neural and liver tissues (04:19)
• How advanced 3D models recreate cell-to-cell interactions, tissue-specific microenvironments, diffusion gradients, and multicellular complexity (10:35)
• Regulatory movements toward reducing animal models, and the challenge of validating advanced alternatives for systemic biology studies (09:10)
• Key differences in designing bioreactors for various cell types, with practical examples from liver and neural models (15:16)
• The impact of scalable, robust 3D models on accelerating drug development and improving selection of candidate therapies (17:22)

Key Takeaway:

Bioprocess development starts when you choose the model that validates your therapeutic approach. If that model can't capture the biology that matters, every downstream optimization is built on a flawed foundation.

In Part 2, Catarina reveals how 3D tumor microenvironments expose drug resistance mechanisms invisible in 2D cultures, and her vision for AI-powered digital twins enabling personalized medicine.

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For more CMC development insights, visit smartbiotechscientist.com.

Connect with Catarina Brito:

LinkedIn: www.linkedin.com/in/catarina-brito-ibet

Advanced Cell Models Lab – iBET: www.ibet.pt/laboratories/advanced-cell-models-lab

Next step:

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The biotech industry stands on the verge of a radical transformation thanks to artificial intelligence (AI) and machine learning (ML). But even the most sophisticated algorithms are only as smart as the data feeding them.

David Brühlmann sits down with Troy Lionberger, Chief Business Officer at A-Alpha Bio, whose team has quietly shattered the data ceiling by measuring and curating more than 1.8 billion protein interactions. Troy Lionberger brings an insider’s perspective from the frontlines of machine learning-powered drug discovery. From partnering with leading biotechs to redesigning classic antibodies for previously “impossible” targets, Troy’s work pushes the edges of what’s tractable in biologic therapeutics.

What you'll hear in this episode:

• Limitations of public data sources like the Protein Data Bank and their impact on current protein engineering approaches (03:11)
• Why combining energetic (ΔG) and structural data matters for building predictive protein engineering models (05:43)
• A-Alpha Bio’s approach to generating 1.8 billion protein interaction measurements for machine learning—what this enables today and what’s possible next (06:30)
• Examples of how A-Alpha Bio’s platform solves challenging therapeutic problems, such as optimizing molecules for 800+ HIV variants and engineering dual-specific antibodies (07:36)
• The ongoing debate: What capabilities should biotech companies keep in-house, and what works best outsourced to service providers? (09:59)
• The potential of synthetic epitopes as vital tools for training models beyond the Protein Data Bank—introducing the Synthetic Epitope Atlas (12:09)
• Key takeaways for scientists: the importance of diligence amidst rapidly evolving AI claims, and advice for accelerating R&D with the right data (14:57)

Wondering how to move protein therapeutics from “interesting” to “impactful” without waiting for years of crystal structures? Listen in to learn how you can harness next-gen machine learning tools and custom datasets for your development projects.

Connect with Troy Lionberger:

LinkedIn: www.linkedin.com/in/troylionberger

A-Alpha Bio website: www.aalphabio.com

Next step:

Need fast CMC guidance? → Get rapid CMC decision support here

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