Tech Transfer for Biopharmaceutical Manufacturing

Introduction to Tech Transfer

• Definition of Tech Transfer
• Tech Transfer: A Project with Challenges

Session 1 : Formal Aspects

• Regulatory Concerns; Product Comparability
• Tech Transfer Procedure, Protocol & Report

Session 2: Tools for Tech Transfer

• Transfer of Information
• Dealing with Scale Up and Changes
• Analytical Transfer

Session 3 : Complexities of Tech Transfer

• Complexity and Risk Factors
• The Importance of Communication
• Optimising the Transfer Programme

Session 4: Workshop: Tech Transfer in the Life -Cycle

Pharma 4.0 is the perceived enabler to accelerate the product life cycle, ensure swift process validation, avoid fail batches and optimize productivity. However, for this, process understanding, the link between CPPs, CQAs and KPIs, need to be identified and deployed, which is still unresolved in industrial practice. This is due to the complexity of the data sources, the missing methods of data contextualization and due to the interactivity of a multitude of unit operations. The commonly accepted hypothesis is that sound data science and digital twin approaches along Pharma principles will be a success factor in this endeavor.

Course Outline:
Hands-on learning of basic principles and best practices performing data analytics and data management for integrated bioprocesses. Bring your own laptop. You will perform exercises on bioprocess data management and analytics using the web-based educational software tool PAS-X Savvy®. Methods and best practices embedded in workflows based on data science ad digital twins, using case studies and hands-on exercises. Designed for the biopharmaceutical and industrial biotech industry.

Digital Bioprocess Lab/ Plant: Best Practices for Bioprocess Data Management

• Data management workflows
• Handling data of different frequencies and dimensionality
• Monitoring & trending, raw data visualization
• Inspecting your data for outliers

Bioprocess Data Analytics: Best Practices

• Setting process phases
• Conversion of raw data to reliable feature based information
• Integrated process analysis over multiple unit operations
• Root cause analysis of process variability
• CPV and golden batch analysis

Digital Twins for CMC Knowledge Capture and Process Control

• Workflows for generation of digital twins
• Applications of digital twins in efficient experimental designs
• Applications of digital twins for process prediction and control
• Integrated process modelling for a robust control strategy