(AI) Artificial Intelligence in a GxP Environment - Live Online Training With a half-day Pre-Training Course on IT Basics for AI

(AI) Artificial Intelligence in a GxP Environment - Live Online Training

Course No 21232

This course is part of the GMP Certification Programme "ECA Certified Computer Validation Manager". Learn more.



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Tel.: +49 (0)6221 / 84 44 0 E-Mail: info@gmp-compliance.org


Haluk Dönmez, B. Braun, Melsungen, Germany
Christophe Girardey, wega Informatik, Basel, Switzerland
Dr Mario Holl, INSPECTIFAI, Karlsruhe, Germany
Julius Kittler, Merck, Darmstadt, Germany
Dr Hadj Latreche, F. Hoffmann-La Roche, Basel, Switzerland
Stefan Münch, Körber Pharma Consulting, Karlsruhe, Germany
Yves Samson, Kereon, Basel, Switzerland
Nicolas Schaltenbrand, Wega Informatik, Basel, Switzerland
Thomas Singer, Merck, Darmstadt, Germany
Dr Arno Terhechte, GMP Inspectorate / Bezirksregierung Münster, Germany


Why should you participate in this event?
  • You will learn the basics of AI / ML and its applicability in the GxP Environment
  • How can pharmaceutical basics, e.g. risk management and qualification / validation be applied to AI? You will experience first approaches!
  • Are relevant pharmaceutical regulations adapted to this new technology and what expectations does an inspector have during an inspection? First concepts will be presented!
  • In case studies, pharmaceutical companies show first practical and practised approaches to the use of AI


At the latest, artificial intelligence (AI) has arrived in the General public since ChatGPT and Bard. Opinions range between absolute euphoria and the invocation of the downfall of humanity. The foundations of AI were laid many years ago and can now be widely implemented due to massively available computing power. The topic has also found its way into the pharmaceutical landscape. First applications have come into operation. The interesting questions here are whether and how this technology is compatible with pharmaceutical regulations, specifications and authorities’ expectations.

Target Group

The Live Online Training is aimed at managers and QA members as well as engineers from the pharmaceutical industry, suppliers and service companies who qualify and operate AI applications in a GxP environment.

Technical Requirements

We use Webex for our live online training courses and webinars. At https://www.gmp-compliance.org/training/online-training-technical-information you will find all the information you need to participate in our events and you can check if your system meets the necessary requirements to participate. If the installation of browser extensions is not possible due to your rights in the IT system, please contact your IT department. Webex is a standard nowadays and the necessary Installation is fast and easy.


Pre-Training Course on IT Basics for AI - 9 October 2023
Introduction to Artificial Intelligence (AI)
Stefan Münch / Yves Samson
  • History of AI
  • Types of AI
  • Real life examples
Introduction to Machine Learning (ML)
Stefan Münch / Yves Samson
  • Technological Basics
  • Different learning / training Methods
  • Example use cases
AI/ML in Pharma, Biotech and Med Devices
Stefan Münch / Yves Samson
  • Challenges for pharmaceutical Industry
  • The GAMP-perspective on AI/ML
  • GMLP: SDLC for AI/ML
  • Use cases / Known scenarios
Data Science as Basis for AI
Nicolas Schaltenbrand & Christophe Girardey
  • Pre-requisites for AI: Data Governance, Data Science, Data Understanding, FAIRification.
  • Specificities in the lab
    • Lack of standardization, Lack of connectivity.
    • Chances with standards: AniML, SiLA
  • Specificities in clinical development
The Regulatory Landscape for AI/ML
Stefan Münch / Yves Samson
  • US / FDA: Expectations and guidance
  • EU / EMA: Expectations and guidance
  • Special case: Generative AI
(AI) Artificial Intelligence in a GxP Environment - 10 October 2023
Regulatory Requirements / Concerns
Dr Arno Terhechte
  • Pharmaceutical laws (AMG and other)
  • EU-GMP Guide Annex 11
  • Concept Paper Revision of Annex 11
  • Software as Medical Device
Validation Approaches
Stefan Münch / Yves Samson
  • Maturity: Increasing autonomy and transferring Control
  • Governance: Developing and operating AI solutions in GxP-regulated areas
Risk Management
Stefan Münch / Yves Samson
  • Power with control: Explaining the outcomes of trained models
  • Applying QRM to development and operation of AI applications
Regulatory Requirements / Assessment
Dr Arno Terhechte
  • Inspection strategy
  • What do inspectors expect from the regulated user?
Case Study: Predictive Control of Yield & Titer
Dr Hadj Latreche
  • Apply Advanced Analytics to enable predictive Titer/Yield and reduce variability while increasing mean toward high-end value
  • In-Flight predictive and adaptive process oversight for shop floor to target Titer/Yield Golden Batches
  • Prove the value of utilizing Advanced Analytics as a digital product leveraging different data sources and advanced predictive algorithms
  • Build site future capabilities required for a sustainable way of working using Advanced Analytics
Case Study: AI in Medical Device Area
Christophe Girardey
  • Introduction on the regulations in Medical Device area
  • AI in Medical Device:
    • Patient risk: more direct than in Pharma?
    • Reality not future: FDA list of devices released.
  • Guidelines on AI:
    • (FDA GMLP > optional as already covered in one of the other sessions)
    • AI & Cybersecurity (ENISA guideline)
    • NMPA Guideline on AI
  • Examples of a use case:
    • Electrocardiogram analysis with AI
Case Study: Revolutionizing Visual Inspection with Artificial Intelligence
Dr Mario Holl
  • Pain points in visual inspection
  • A machine-agnostic AI solution Framework
  • Strategies for developing robust and reliable AI models
  • Qualification and necessary documentation
  • Results of AI powered visual inspection
Case Study: Challenges and Limitations of Machine Learning Systems in Automated Visual Inspection Systems
Haluk Dönmez
  • Introduction and Basics
  • Application and Challenges
  • Approach and ML Training
  • Testing and Qualification
  • Conclusion
Enhancing Production Efficiency: An End-to-End Process Perspective through Data Science
Julius Kittler / Thomas Singer
  • Introduction and Overview of the Use Case
  • Consolidating the Tablet Production Process into a Comprehensive Dataset
  • Theoretical Basics of Machine Learning and Gradient Boosting Decision Trees
  • Application of Machine Learning to Identify Critical Factors Impacting Production Target Variables
  • Key Takeaways and Lessons Learned

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