Advances in High-Throughput Upstream Process Development

Patricia D. McNeill from Lundbeck Seattle BioPharmaceuticals emphasized the importance of Design of Experiment (DOE) strategies in optimizing drug development efficiency. By leveraging screening designs, advanced machine learning, and the Fractionally Weighted Bootstrapping method, companies can streamline the process from pre-clinical research to late-stage development, reducing costs and time.

Key Insights

• Utilizing DOE strategies and auto-validation for small data sets can save time and resources, speeding up time to market.
• Selecting the right cell line early on and optimizing processes during Phase I clinical trials are crucial milestones.
• Pre-DOE homework, such as evaluating process variability and analytics, is essential for setting experimental factor ranges.
• Collaboration, establishment of Communities of Practice (COPs), and transparent decision-making processes are vital for successful implementation of data management strategies in biotech production.
• Applying machine learning to small data sets can provide valuable insights for process optimization.