This is a full day course from 9am - 5pm.

Through a series of presentations, case studies and interactive discussions and exercises this course will focus on CDMO oversight- selection, negotiation, tech transfer and life cycle management specifically for cell and gene therapy products.

Some of the topics to be discussed include:

CDMO Selection: Models and Process
Contract and Quality Agreement Negotiation
Tech Transfer: Best practices and risk management
Quality Assurance and Quality Control
On- Going Manufacturing: Organisational models and interactions
Life Cycle Management
Relationship Management

This is a full day course from 9am - 5pm.

Course Objective: This quick but comprehensive tutorial will provide an overview of the regulatory and quality principles that guide the analytical studies for all biological products, with emphasis on the specific elements applicable to complex MODALITIES such as gene and cell therapy. Emerging best practices in analytical methods for characterization, comparability, release and stability testing of gene and cell therapy will be presented. The rationale behind the requirements, with supporting references, will be provided. Attendees to this class will receive electronic copies of all reference guidances and publications discussed in the class.


Course Outline:
• Overview of CMC analytical regulatory deliverables for all biological products
• Specific additional guidances applicable to analytics of gene and cell therapy products
• Core principles of analytical characterization, comparability and control of complex biological products
• Practices for establishing the right ‘analytical tool kit’ for gene and cell therapy products
• Challenges in establishing product reference standards for use with complex biologics
• Emerging horizontal method standards for use with gene and cell therapy product analytical methods
• Key elements of compendial and non-compendial analytical methods lifecycles for biological products
• Quality requirements for documentation and data integrity of gene and cell therapy analytical studies
• Interactive Q&A with class speaker

This is a full day course from 9am - 5.30pm

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