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

Next step:

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

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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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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/jesuszurdo

Email: jesus.zurdo@gmail.com

Next step:

Book a 20-minute call to help you get started on any questions you may have about bioprocessing analytics: https://bruehlmann-consulting.com/call

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