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

Dec 11, 2025

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Giuseppe Licari

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.

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INSIGHT

Watch The Protein Movie

Use molecular dynamics to watch the protein 'movie' rather than rely on a static structure.
Simulations reveal how excipients and buffers interact with the protein and affect stability.

INSIGHT

Short Simulations Predict Long-Term Trends

In-silico tools cannot directly compute long-term shelf life because simulations cover short timescales.
But short simulations can reveal molecular properties that correlate with long-term stability.

INSIGHT

AI Needs Data, But Helps Early Design

Machine learning needs large, diverse datasets before it can robustly predict formulation behaviors across proteins.
Generative AI already helps design proteins with fewer liabilities and better developability upstream.

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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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