Design of experiments (DoE) in protein purification (part 1)

Unlock the power of Design of Experiments (DoE) in optimizing protein purification experiments with this comprehensive introductory session. Part 1 of this series explores the fundamentals of DoE, including key terminology, experimental design strategies, and practical examples specific to protein purification systems. By applying Quality by Design (QbD) principles, you can gain deeper insights into your processes, optimize performance, and enhance reproducibility. What you’ll learn in this video: • An introduction to DoE in protein purification and why it is essential for bioprocess development. • Examples of experimental designs and their objectives in defining process spaces. • Insights into DoE models, including full factorial, fractional factorial, and central composite designs, tailored to chromatography applications. • A step-by-step explanation of critical factors, responses, and verification techniques to ensure robust outcomes. With practical examples and expert guidance, this session demonstrates how DoE can streamline protein purification workflows, making experimentation more efficient and results more reliable. Download the free handbook, "Design of Experiments in Protein Production and Purification" (no registration required): https://www.cytiva.com/handbooks Learn more about our DoE tools and solutions: https://www.cytivalifesciences.com/en/us/solutions/bioprocessing/products-and-solutions/accelerate-biopharmaceutical-process-development/design-of-experiments Access specialized training in bioprocess development: https://www.cytivalifesciences.com/en/us/training/training-catalog/object/course-5064846 Discover how to elevate your protein purification processes by mastering DoE—an essential tool for achieving consistency, efficiency, and innovation in biopharma research. Chapters: 00:00:00 Introduction to Design of Experiments (DOE) 00:00:01 Understanding process inputs and outputs 00:03:28 Understanding process inputs and interactions 00:05:18 Understanding interaction effects in Design of Experiments 00:07:13 Understanding DOE terminology and factors 00:08:51 Understanding model transfer functions in chromatography 00:10:34 Optimizing chromatography in downstream processing 00:12:17 Key factors in process development 00:14:04 Understanding design space and optimization in QbD 00:15:46 Understanding robustness testing in experimental processes 00:17:28 Understanding transfer functions and polynomial models 00:19:24 Understanding interaction effects in statistical models 00:21:41 Understanding two-factor interaction effect in protein purification 00:23:07 Impact of pH and conductivity on aggregate removal 00:24:50 Optimizing conductivity and pH for aggregate removal 00:26:38 Importance of replicating center points in experiments 00:28:22 Determining the need for quadratic models in experimental design 00:30:08 Understanding error terms in predictive models 00:32:00 Scaling up lab models to pilot scale 00:33:46 Understanding fractional factorial designs 00:35:28 Understanding central composite design in polynomial modeling 00:37:41 Understanding Design of Experiments: key factors and techniques 00:38:46 Exploring fractional factorial design in process analysis 00:40:25 Conclusion of lecture part 1 #proteinpurification #biopharma #lifesciences