Digitalisation & Artificial Intelligence

Objectives

Reasons to attend this conference:

• You will get an overview of the current state of digitalisation and use of artificial intelligence in the pharmaceutical industry.
• You will learn how efficiency can be improved and quality can be supported through the implementation of digitalisation.
• In various case studies of pharmaceutical companies, projects from practice are presented.

Registration

Target Audience

The event is directed at specialists from the pharmaceutical industry as well as at engineers and planners who have to deal with digitalisation and AI projects.

It particularly addresses the departments:

• IT
• Production
• Quality assurance
• Engineering / Technology

Moderators

Stefan Münch, Körber Pharma Consulting
Yves Samson, Kereon

Tuesday, 8 April 2025

09.00 - 17.30 h

Accelerate Automation Project Implementation and Reduce Risk with a Digital Twin
Rasmus Wendelboe Jørgensen, Novo Nordisk
Vicky Athanasiou, Emerson

• What were the Novo Nordisk expansion project objectives?
• The solution provided
• The issues it helped to solve and the benefits of the Digital Twin use (project acceleration and automation error reduction)
• Any challenges during the implementation and how they were overcome
• Recommendations for future implementations
  
  

Live Demos

Virtual Pharma Campus
Pharmaplan

Digital Assistant for Maintenance at the Shopfloor
ZETA

Going to Market Faster in Life Sciences, Leveraging the Emerson Digital Twin
Emerson Automation Solutions

Kneat Gx: Live Digital Validation Software Demonstration
Kneat Solutions

Case Study: Design & Implementation of a new highlyautomated modular OSD Production Facility at Bayer Leverkusen
Andreas Bail, Bayer
Anton Kopitzsch, Glatt

• Highly automated Package Units with fully digitized shop floor integration over ISA 95 levels (MES, MFC, PLC...)
• State of the art interface communication
• Automated material-handling at shop floor-level with AGVs, automated lifting columns, docking-systems and robotics
• Glatt developed new intra-logistics (orchestration) system
• Comprehensive data access on all generated production data
• Readiness for future AI use cases such as predictive maintenance, near real time operational support and continuous improvement
  
  

Smart Panel – The Future of the pharmaceutical Production Process
Christoph Dechow, Boehringer Ingelheim Pharma
Dr Sebastian Wibbeling, Fraunhofer-Institut for Material Flow and Logistics IML

• Aim: The digitization of pharma container processes supports production by increasing transparency and controllability
• The result - the Smart Panel is a device that provides information for the worker at the pharma container and enables processes to be initiated directly on site without a PC or personalized handheld
• In a fully connected world, the Smart Panel acts as the main device for process management by addressing the topics of efficiency, transparency, and security on the shop floor
  
  

Continued Process Verification Using Automated Data Assessment
Dr Philip Hörsch, Vetter Pharma-Fertigung
Bettina Schroeder, Vetter Pharma-Fertigung

• Challenges in a CPV program for approx. 100 products from multiple customers as a CDMO
• Harmonized approach for risk-based data assessments in a validated environment
• Introduction of a statistical software with automated data trending and report functions. What can be automated for a wide range of various process parameters?
• Challenges during implementation of the software and problem solutions
• Outlook and future opportunities

Wednesday, 9 April 2025

09.00 - 17.00 h

Digitalisation and AI from the Inspector’s Point of View
Dr Arno Terhechte, Bezirksregierung Münster

AI (Artificial Intelligence) in Manufacturing
Dr Monika Hupfauf, KOCH / HUPFAUF Attorneys-at-Law
Amir Abou Elmagd, Genome Lawyers

• What are GMP regulated activities and is the use of AI under GMP possible?
• Is Eudralex Volume 4 Annex 11 specific enough in this context?
• What is the input of the European Medicines Agency (EMA) on AI in manufacturing: does EMA regulate or guide us?
• How does the AI Act come in the context of manufacturing?
• Use of AI in manufacturing – responsibilities of Qualified Persons regarding batch release?
• What else is relevant in the AI workplan by EMA?
• Last but definitely not least: Liabilities when using AI in manufacturing - whose responsibility are we talking about?
  
  

Benefits and Challenges in Developing a GenAI Solution for a GxP-relevant Process
Dr Rolf Roth, Merck Healthcare
Stephane Guillet, Merck Healthcare

• Ideation and Prioritization of AI use cases
• Insights into an advanced GenAI prototype for a GxP-relevant process
• Challenges for GxP validation
• Outlook / Discussion
  
  

Artificial Intelligence (AI) for Discrepancy Management
Dr Philipp Fey, Boehringer Ingelheim
Jorge Gil-Hernandez, Boehringer Ingelheim

• Detection of clusters based on Natural Language Processing
• Validated AI tools in GxP environment
• Human centric approach to ensure quality and control
• Risk based approach to reduce work-load for investigators
  
  

Application of dynamic Learning Systems to increase Efficiency Increase in Pharma Production Lines
Felix Georg Müller, plus10
Martin Heitmann, d-fine

• When online learning and situationally acting tools can help and when not
• Overview of dynamic system use cases in a GMP environment
• AI-based behavior learning on high frequency machine data of whole production lines
• Challenges and approaches to support governance of non-design-freeze approaches
• Case study insights regarding obstacles and learnings about validation, implementation and operations
  
  

AI in Medical Image Processing
Daniel Wolf, Ulm University Medical Center

• Introduction to deep learning models for imaging
• Deep learning for diagnosis and prognosis
• Pre-training of deep learning models
• Explainable Artificial Intelligence