Smart Biotech Scientist | The CMC and Bioprocessing Podcast for Process Development and Manufacturing Leaders

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-ibetAdvanced Cell Models Lab – iBET: www.ibet.pt/laboratories/advanced-cell-models-labNext step:Need fast CMC guidance? → Get rapid CMC decision support hereSupport the show

Catarina Brito, a Principal Investigator at ITQB NOVA and expert in advanced 3D cell models, dives deep into the limitations of traditional 2D cultures and animal models for preclinical research. She highlights how her innovative neural, liver, and tumor models capture the complexity of human biology, which can dramatically improve drug predictivity. Catarina also discusses the importance of bioreactor design tailored to tissue needs and how scalable models can lead to faster, more relevant testing in drug development.

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/troylionbergerA-Alpha Bio website: www.aalphabio.comNext step:Need fast CMC guidance? → Get rapid CMC decision support hereSupport the show

Antibody therapeutics have transformed modern medicine, but for many scientists, developing new candidates still feels like searching for a needle in a haystack—a slow, expensive, and unpredictable process. Structural biology and high-throughput data generation are now collapsing that haystack, offering unprecedented visibility into the molecular handshake that drives life: protein-protein interactions.In this episode, David Brühlmann sits down with Troy Lionberger, Chief Business Officer at A-Alpha Bio, for an in-depth discussion on protein-protein interactions and how advances in data generation and machine learning are transforming antibody discovery and drug development.Troy Lionberger shares his journey into biotechnology, challenges long-held beliefs about antibody development, and explains how Alphabio's high-throughput affinity measurements are shortening timelines and improving outcomes for therapeutic development.In this episode, you’ll learn about:The historical and current challenges in characterizing these interactions at scale (06:22)How new technologies—especially high-throughput platforms—are changing the needle-in-the-haystack approach (08:40)A comparison of traditional in vivo and in vitro antibody discovery methods, along with their strengths and limitations (09:06)The evolving role of AI and machine learning in antibody discovery and lead optimization (12:11)Real-world examples of how A-Alpha Bio’s approach is compressing years of work into months without sacrificing quality (13:58)The science behind A-Alpha Bio’s AlphaSeq technology and how it leverages yeast display and genomics for large-scale affinity measurements (20:43)The practical affinity range the technology can measure, covering most therapeutic applications (23:25)Whether you’re a scientist navigating CMC or a biotech professional curious about next-generation workflows, this episode offers practical insights into both traditional and emerging methodologies in the field.Connect with Troy Lionberger:LinkedIn: www.linkedin.com/in/troylionbergerA-Alpha Bio website: www.aalphabio.comNext step:Need fast CMC guidance? → Get rapid CMC decision support hereOne bad CDMO decision can cost you two years and your Series A. If you're navigating tech transfer, CDMO selection, or IND prep, let's talk before it gets expensive. Two slots open this month.Support the show

For generations, silkworm pupae were discarded as waste from silk production. Now, KAICO is proving these organisms can function as highly efficient protein factories—producing complex vaccine antigens at yields and costs that challenge conventional bioreactor-based manufacturing. With a PCV2 oral vaccine already registered in Vietnam and a human norovirus vaccine preparing for Phase I clinical trials, the silkworm platform is moving from proof-of-concept to commercial reality.In Part 2, Masafumi Osawa, Business Development Lead at KAICO, walks us through the company's product pipeline, the regulatory landscape for this unprecedented platform, and why silkworm-based manufacturing could reshape global vaccine accessibility. From farm-scale validation to regulatory dialogue with Japan's PMDA, this episode bridges platform science with product development.Stops along our Silk Road to biomanufacturingKAICO’s approach to expressing complex proteins, including oral and injectable vaccines for animals and clinical-stage human health products (00:40)Immune-enhancing feed additive for pigs against PCV2 (registered in Vietnam), companion animal products, and human norovirus vaccine entering Phase 1 trials (02:50)Advantages of silkworm-produced antigens for both injectable and oral vaccines, and comparison to plant-based systems (04:57)How silkworm production enables rapid scale-out and high-yield protein expression for global accessibility (05:34)Speed of vaccine development with the silkworm platform—example with SARS-CoV-2 recombinant protein produced in three months (08:08)Key regulatory differences between animal and human vaccine development, including country-specific classification and global harmonization efforts (09:14)Sustainability and distributed manufacturing potential of silkworm-based systems (11:10)Milestones toward the first human biologic produced in silkworms, with phase 1 trials starting soon (12:27)Protein yield per silkworm pupae—scalability advantages compared to conventional bioreactor approaches (14:04)Masafumi Osawa’s thoughts on the future of silkworm-based biologics: from democratized therapies to personalized medicines (15:15)Want to know if silkworms can solve your tough protein expression problems? Tune in now to learn how KAICO’s biotech is set to redefine what’s possible for vaccine development, and how their distributed, cost-effective approach could open doors across sectors.Connect with Masafumi Osawa,:LinkedIn: www.linkedin.com/in/masa-osawaKAICO Ltd.: www.kaicoltd.jp/enNext step:Need fast CMC guidance? → Get rapid CMC decision support hereSupport the show

For over 4,000 years, silkworms have connected civilizations through ancient trade routes. Now, KAICO Ltd., a Japanese biotech spin-off from Kyushu University, is transforming these creatures into living bioreactors capable of producing complex recombinant proteins and vaccine antigens—without the bioreactors, expensive media, or massive water consumption of conventional platforms.Masafumi Osawa, Business Development Lead at KAICO, brings an unconventional path to biotech. Trained in cultural anthropology with fieldwork experience in Indonesia, he witnessed firsthand the healthcare disparities that drive his current mission. After years in pharmaceutical business development at Towa Pharmaceutical, he joined KAICO to help scale a technology he believes could reshape global vaccine accessibility. His cross-cultural expertise now proves invaluable as KAICO expands internationally, with active partnerships in Vietnam and Thailand and growing interest from other regions.Episode highlights:Masafumi’s transition from anthropology to biotech, and how cross-cultural expertise benefits global health collaborations. (04:15)The founding story of KAICO, spun out from Kyushu University and focused on recombinant proteins and vaccine antigen production (08:45)Step-by-step overview of the silkworm baculovirus expression system, including pupae handling and bioprocessing basics. (10:28)Practical differences between silkworms, E. coli, mammalian, and insect cell culture platforms—exploring advantages and drawbacks. (13:10)Strategies KAICO uses to control silkworm variability, including SPF grade sourcing, diet, environment, and documentation for pharmaceutical acceptance. (15:08)Production scalability: a single pupa can match 100–1000 ml of insect cell culture, with major implications for cost and environmental footprint. (16:42)Downstream harvesting and purification—how KAICO extracts and processes proteins from silkworm pupae, keeping methods largely familiar to traditional systems. (19:31)Regulatory and GMP challenges of using live organisms, and KAICO’s approach to satisfying authorities and ensuring product consistency. (21:43)Whether you’re curious about alternative biomanufacturing methods or want a transparent look at silkworm-based protein expression from research to the clinic, this episode delivers practical insights and thoughtful discussion.Connect with Masafumi Osawa:LinkedIn: www.linkedin.com/in/masa-osawaKAICO Ltd.: www.kaicoltd.jp/enNext step:Need fast CMC guidance? → Get rapid CMC decision support hereOne bad CDMO decision can cost you two years and your Series A. If you're navigating tech transfer, CDMO selection, or IND prep, let's talk before it gets expensive. Two slots open this month.Support the show

Biomanufacturing has always dealt with the challenge of turning vast, complex datasets and intricate production steps into life-changing therapies. But when batch records multiply and process deviations loom, how do biotech teams make sense of it all? In this episode, we move beyond theory to the nuts and bolts of how AI - when thoughtfully deployed - can turn bioprocessing chaos into actionable intelligence, paving the way for the factory of the future.Our guest, Ilya Burkov, Global Head of Healthcare and Life Sciences Growth at Nebius AI, doesn’t just talk about data wrangling and algorithms—he’s spent years building tools and strategies to help scientists organize, contextualize, and leverage real-world datasets. Having worked across tech innovation and pharmaceuticals, Ilya Burkov bridges cutting-edge computation with the practical realities of CMC development and manufacturing, making him a trusted voice on how bioprocessing is rapidly changing.Highlights from the episode:Advice for biotech scientists on learning from innovations in other industries (00:02:21)Tackling the complexities of organizing huge and often unstructured datasets in bioprocessing (03:08)Techniques and tools to structure, label, and prepare data for AI—including Nebius’s in-house tool, Tracto AI (06:24)Strategies for startups and small teams—how to begin implementing AI and what areas of bioprocessing to focus on first (10:12)The vision for the “factory of the future”: AI-driven, interconnected, and self-learning manufacturing environments (08:11)Navigating the decision between on-premise and cloud computing for scalable, cost-effective AI workloads (12:32)The importance of partnership between scientists and AI, emphasizing collaboration and data-driven decisions (00:15:47)Wondering how to kick off your own AI-enabled bioprocessing project, or what to insource versus outsource as you scale? This episode gives you a grounded starting point—minus the buzzwords and empty promises.Connect with Ilya Burkov:LinkedIn: www.linkedin.com/in/ilyaburkovContact email: ilya.burkov@nebius.comNebius: www.nebius.comIf this topic grabbed you, you'll love these related episodes focusing on advanced modeling, continuous manufacturing, and Digital TwinsEpisodes 213 - 214: From Developability to Formulation: How In Silico Methods Predict Stability Issues Before the Lab with Giuseppe LicariEpisodes 85 - 86: Bioprocess 4.0: Integrated Continuous Biomanufacturing with Massimo MorbidelliEpisodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael SokolovEpisode 153 - 154: The Future of Bioprocessing: Industry 4.0, Digital Twins, and Continuous Manufacturing Strategies with Tiago MatosEpisodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist GernaeyNext step:Need fast CMC guidance? → Get rapid CMC decision support hereSupport the show

Across biotech labs, researchers swim in oceans of process data: sensor streams, run records, engineering logs, and still, crucial decisions get stuck in spreadsheets or scribbled into fading notebooks. The challenge isn’t having enough information, it's knowing which actions actually move the needle in cell culture productivity, process stability, and faster timelines.This episode, David Brühlmann brings on Ilya Burkov, Global Head of Healthcare and Life Sciences Growth at Nebius AI. With a career spanning NHS medicine, regenerative research, and cloud infrastructure, Ilya Burkov has lived the leap from microscope to server room. He’s seen firsthand how digital twins, autonomous experimentation, and cloud-first strategies are shifting the way biologics are developed and scaled.Topics discussed:Shifting from experimental-based to computational bioprocess development, and the evolving role of human expertise vs. AI (02:48)Ilya Burkov's journey from medicine and orthopedics to AI and cloud infrastructure (04:15)Solving data silos and making real-time decisions with digital twins and automated analytics (06:36)The impact of AI-driven lab automation and robotics on drug discovery timelines (08:51)Using AI to accelerate cell line selection, cloning, and protein sequence optimization (10:12)Why wet lab experimentation is still essential, and how predictive modelling can reduce failure rates (11:15)Reducing costs and accelerating development by leveraging AI in process screening and optimization (12:32)Strategies for smaller companies to effectively store and manage bioprocess data, including practical advice on cloud adoption and security (14:30)Application of AI and digital twins in scale-up processes, and connecting diverse data types like CFD simulations and process data (17:18)The ongoing need for human expertise in interpreting and qualifying data, even as machine learning advances (19:09)Wondering how to stop your own data from gathering dust? This episode unpacks practical strategies for storing and leveraging your experimental records - whether you’re in a major pharma or a small startup with limited tech resources.Connect with Ilya Burkov:LinkedIn: www.linkedin.com/in/ilyaburkovContact email: ilya.burkov@nebius.comNebius: www.nebius.comIf this topic grabbed you, you'll love these related episodes focusing on advanced modeling, continuous manufacturing, and Digital TwinsEpisodes 213 - 214: From Developability to Formulation: How In Silico Methods Predict Stability Issues Before the Lab with Giuseppe LicariEpisodes 85 - 86: Bioprocess 4.0: Integrated Continuous Biomanufacturing with Massimo MorbidelliEpisodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael SokolovEpisode 153 - 154: The Future of Bioprocessing: Industry 4.0, Digital Twins, and Continuous Manufacturing Strategies with Tiago MatosEpisodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist GernaeyNext step:Need fast CMC guidance? → Get rapid CMC decision support hereSupport the show

Giuseppe Licari, a Principal Scientist specializing in computational structural biology at Merck KGaA, shares insights on implementing in silico methods to improve protein formulation. He discusses predicting stability issues before lab trials, using molecular dynamics to simulate protein behavior over time, and integrating computational tools with experimental studies. Giuseppe highlights the limitations of current methods and the potential of AI in enhancing protein developability, providing actionable strategies for both large pharma and startups to streamline their development processes.

What if you could predict formulation failures before ever touching a pipette? Computational approaches are revolutionizing biologics development, replacing trial-and-error experimentation with predictive intelligence that catches stability issues early and accelerates your path from candidate selection to clinic.In this episode, David Brühlmann welcomes Giuseppe Licari, Principal Scientist in Computational Structural Biology at Merck KGaA. A chemist by training, Giuseppe transitioned from wet lab experimentation to the predictive power of in silico modeling. Today, he operates at the intersection of computational biology and CMC development, using digital tools to screen candidates for developability, predict formulation challenges, and de-risk development programs before committing resources to the lab.Discover how computational methods are transforming the way biotech companies approach developability assessment and formulation strategy:Why maximizing shelf life isn’t always necessary in early development phases (02:56)The critical role of communication between computational and bench scientists (06:46)Core properties to assess for developability, including hydrophobicity, aggregation, charge, and immunogenicity (11:06)How accurate are in silico predictions, and where do they add the most value? (13:23)The limitations and strengths of machine learning and physics-based models in predicting protein behavior (15:19)The differences between developability, formulation development, and formulatability, and the value of early cross-functional collaboration (17:17)When to use platform formulations and when tailored approaches are needed for complex molecules (19:25)The advantages of using computational methods at any stage, especially for de-risking strategies (20:13)Listen in for practical strategies for integrating in silico predictions into your developability and CMC workflows, catching stability issues before the lab, and making smarter development decisions that save time, material, and money.Connect with Giuseppe Licari:LinkedIn: www.linkedin.com/in/giuseppe-licariNext step:Need fast CMC guidance? → Get rapid CMC decision support hereOne bad CDMO decision can cost you two years and your Series A. If you're navigating tech transfer, CDMO selection, or IND prep, let's talk before it gets expensive. Two slots open this month.Support the show