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/ilyaburkov

Contact email: ilya.burkov@nebius.com

Nebius: www.nebius.com

If this topic grabbed you, you'll love these related episodes focusing on advanced modeling, continuous manufacturing, and Digital Twins

• Episodes 213 - 214: From Developability to Formulation: How In Silico Methods Predict Stability Issues Before the Lab with Giuseppe Licari
• Episodes 85 - 86: Bioprocess 4.0: Integrated Continuous Biomanufacturing with Massimo Morbidelli
• Episodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael Sokolov
• Episode 153 - 154: The Future of Bioprocessing: Industry 4.0, Digital Twins, and Continuous Manufacturing Strategies with Tiago Matos
• Episodes 173 - 174: Mastering Hybrid Model Digital Twins: From Lab Scale to Commercial Bioprocessing with Krist Gernaey

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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-licari

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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-licari

Next step:

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

One 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.

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