Smarter Drug Development for Biologics with In-Silico Modelling

Advancing Formulation and Stability Optimization

The development of new protein therapeutics is a complex and costly process, often hindered by late-stage failures due to poor drug-like properties or instability of drug candidates. In silico modeling has emerged as a powerful tool to identify potential risks early, analyze structural properties, and inform formulation optimization strategies. The integration of computational approaches with experimental developability assessments enables data-driven decision-making, reducing development time, costs and material constraints. This webinar explores recent advances in in-silico tools and demonstrates how computational methods streamline drug development and enhance formulation success.

In the first part of the session, Tim Menzen, Chief Technology Officer at Coriolis, will provide a general introduction to in silico applications and their key areas of use at Coriolis, including particle identification, lyophilization process development, and developability assessment. He will also discuss the integration of in silico models with experimental approaches, highlighting their combined potential for optimizing drug development.

In the second part, Andrea Arsiccio, Senior Scientist at Coriolis Pharma, will present different case studies to demonstrate the role of in silico modeling in developability assessment and early formulation development. He will demonstrate how these approaches can help minimize risks, shorten development times, and enhance decision-making, offering valuable insights for scientists and researchers.

An interactive Q&A session with the speakers will conclude the webinar.

What challenges this webinar will address

• Costly Late-Stage Failures – Identifying potential stability, solubility, or manufacturability issues early to prevent costly failures in later development phases.
• Time-Consuming and Costly Experimental Work – Guiding laboratory testing and increasing knowledge by leveraging computational predictions.
• Material Constraints in Early-Stage Assessments – Reducing the dependency on physical material for initial assessments.
• Avoiding Wrong Decisions in Formulation Development – Guiding the selection of excipients and processing conditions to enhance drug formulation and stability.

Learning Objectives

• Understand the Role of In Silico Modeling – Gain insights into how computational approaches contribute to drug developability assessments and formulation optimization.
• Reduce Risks in Drug Formulation – Discover how computational predictions can help reduce costs, shorten development timelines, and mitigate risks in preclinical phase and drug product development.
• Explore Case Studies and Applications – Review real-world examples of in silico modeling successfully guiding formulation strategies and overcoming development challenges.