214: From Developability to Formulation: How In Silico Methods Predict Stability Issues Before the Lab with Giuseppe Licari - Part 2

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

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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