GMP & GDP Forum 2023

 

Moderator
Alfred Hunt

It is of key importance that medicinal products are not only made to a high quality in accordance with Good Manufacturing Practice (GMP), but that the quality and integrity of these products are maintained through the entire supply chain to the patient. This is where Good Distribution Practice (GDP) comes into play.
On day 3, challenges and possible solutions will be discussed and examples will demonstrate how the requirements can be put into practice.

GDP Update & Outlook
Alfred Hunt

• Major GDP developments
• Current Trends in Europe and the US

GDP for Veterinary Medicinal Products
Dr Rainer Kahlich

• New regulatory framework for VET-GDP – the European Regulations
• GDP for veterinary / for human medicinal products – what are the similarities, what are the differences?
• Inspection focus and prevalent findings

Disruption of the Global Supply chain: How to still be able to be GDP-compliant
Peter Flury

• The “New Normal” – Unpredictable availability of mode of transport, equipment, carriers and capacity; Failure of global security architecture with high uncertainty and potential interruption of trade lanes and supplies “overnight”
• Strategies to manage and control the “New Normal”
• Usage of Open-source intelligence (OSINT) to understand geo-political risks, anticipate and integrate them into a supply chain strategy

Data Integrity in a Hybrid Working Environment
Alfred Hunt

• How to ensure good documentation practices and data integrity
• Use of document repositories
• Capturing approvals of remote workers
• Managing scanned documents

Validation of Computerized Systems under GDP Regulation
Dr Torsten Schmidt-Bader

• Validation requirements – regulatory overview
• GAMP oriented validation approach of GDP critical systems
• Validation of a new warehouse and material management system – case study 2023: Wholesaler Germany

Case study on Thermostability
Saddam Huq

• Type of thermostability data
• Label claim versus thermostability data
• How thermostability data can be used for supply chain