Smart Biotech Scientist | Master Bioprocess CMC Development, Biologics Manufacturing & Scale-up, Cell Culture Innovation

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: [email protected]: 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: [email protected]: 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

Computational methods can predict stability issues before the lab. But how do you actually implement these approaches in your formulation workflow? From excipient selection to long-term stability prediction, in silico tools are transforming how biotech teams develop robust formulations while reducing costly trial-and-error cycles.In Part 2, Giuseppe Licari, Principal Scientist in Computational Structural Biology at Merck KGaA, returns to share practical implementation strategies for integrating computational methods into biologics formulation development. Giuseppe reveals how molecular dynamics simulations guide excipient selection, where current methods hit their limits, and how emerging AI capabilities are expanding what's possible in formulation prediction.Whether you're at a well-resourced pharma company or a lean startup, Giuseppe offers actionable guidance for leveraging computational tools to predict protein behavior, optimize formulations, and accelerate your development timeline.Topics covered:Predicting protein aggregation and excipient interactions before manufacturing (00:45)Using molecular dynamics to understand protein behavior over time and in different environments (03:03)The interplay between computational predictions and experimental stability studies (04:49)The limitations of current in silico methods for predicting long-term stability (05:08)Emerging use of AI and machine learning to predict protein properties and improve developability (06:36)Future possibilities: Generative AI for protein design and formulation prediction (08:06)Advice for small companies: leveraging software-as-a-service and external partners to access computational tools (09:55)The impact of increasing computational power on the field's evolution (11:12)Most important takeaway: being open and curious about new computational techniques in biotech formulation (12:08)Discover how to bridge computational predictions with experimental validation, navigate the current limitations of in silico stability forecasting, and position your organization to benefit from AI-driven formulation development, regardless of your resource constraints.Connect with Giuseppe Licari to continue the conversation and explore how computational approaches can solve your formulation challenges before you ever step into the lab.Connect with Giuseppe Licari:LinkedIn: www.linkedin.com/in/giuseppe-licariNext step:Need fast CMC guidance? → Get rapid CMC decision support hereSupport the show

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

What happens when cell therapy innovation meets real patient urgency? In this conversation, the barriers between scientist and patient all but vanish, bringing clarity—and a new sense of mission—to some of the biggest problems facing advanced therapy manufacturing and delivery.Meet Jesús Zurdo, a biotech leader whose three decades of experience in innovation took on a whole new perspective when he became a leukemia patient himself. Seamlessly straddling the worlds of industry and patient care, Jesús Zurdo brings a refreshingly honest, systems-level view to cell therapies, manufacturing bottlenecks, and the realities of getting therapies from the lab to bedside.Topics discussed:Experiences and lessons from stem cell registries and point-of-care manufacturing models (03:15)Challenges and potential of autologous and allogeneic cell therapies, including scalability and accessibility (06:08)The promise and limitations of in vivo cell therapy, delivery risks and patient safety (07:06)Reflections on current trends in manufacturing automation, delivery platforms, and the risk of overengineering (09:49)Barriers to wider adoption of advanced cell therapies, including hospital infrastructure and economic constraints (13:31)The case for earlier lines of treatment with new modalities and value in learning from actual patient experiences (14:30)The importance of integrating voices of patients, clinicians, and developers when solving complex problems (17:31)Why urgency and remembering our future roles as patients should guide therapy development (18:51)Ready for bioprocessing to serve patients, yours included? For more insights and hands-on support, please subscribe so you never miss the next perspective-shifting episode.Connect with Jesús Zurdo:LinkedIn: https://www.linkedin.com/in/jesuszurdoEmail: [email protected] step:Need fast CMC guidance? New on-demand CMC advisory → Learn more hereSupport the show