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

29-31 October 2024

Course No. 21786

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Speakers

Klaus Feuerhelm

Klaus Feuerhelm

Regierungspräsidium Tübingen

Objectives

(AI) Artificial Intelligence in a GxP Environment
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
 
(AI) Artificial Intelligence in Visual Inspection
The pharmaceutical industry is increasingly interested in AI for the visual inspection of parenterals to optimize and enhance process efficacy. However, the lack of specific regulatory requirements for AI validation poses challenges from a Good Manufacturing Practice (GMP) perspective, such as data representativeness, model design, and data integrity throughout the product lifecycle.
In visual inspection, AI aims to improve efficiency by reducing the false acceptance rate (FAR) of defect units and the false reject rate (FRR) of good units, which together determine the misclassification rate and the inspection process‘s effectiveness. A high FAR is associated with a possible quality risk, while the FRR is a measure of the economic damage of the selected control process.
Despite its potential, the FDA‘s guidance on automated systems mentions AI only briefly, highlighting the need for comprehensive regulation and addressing technical challenges like training, domain knowledge, and data quality. Implementing AI systems requires specialized expertise, precise data labelling, and cloud computing for model training.
At this online conference, we will be focussing on GMP regulation and technical aspects. Questions such as
  • Does an automatic AI solution eliminate the need for manual visual inspection?
  • When does the use of Artificial Intelligence make sense?
  • What expectations can be placed on the achievable false
  • reject rates of AI-supported inspection systems?
  • Are there applications or technical limitations that even AI-supported systems cannot solve?
  • How does a project to switch to AI-based visual inspection work?
  • What GMP authority requirements are there for such systems?

Background

(AI) Artificial Intelligence in a GxP Environment
The pharmaceutical industry is increasingly interested in AI for the visual inspection of parenterals to optimize and enhance process efficacy. However, the lack of specific regulatory requirements for AI validation poses challenges from a Good Manufacturing Practice (GMP) perspective, such as data representativeness, model design, and data integrity throughout the product lifecycle.
In visual inspection, AI aims to improve efficiency by reducing the false acceptance rate (FAR) of defect units and the false reject rate (FRR) of good units, which together determine the misclassification rate and the inspection process‘s effectiveness. A high FAR is associated with a possible quality risk, while the FRR is a measure of the economic damage of the selected control process.
Despite its potential, the FDA‘s guidance on automated systems mentions AI only briefly, highlighting the need for comprehensive regulation and addressing technical challenges like training, domain knowledge, and data quality. Implementing AI systems requires specialized expertise, precise data labelling, and cloud computing for model training.
At this online conference, we will be focussing on GMP regulation and technical aspects. Questions such as
  • Does an automatic AI solution eliminate the need for manual visual inspection?
  • When does the use of Artificial Intelligence make sense?
  • What expectations can be placed on the achievable false
  • reject rates of AI-supported inspection systems?
  • Are there applications or technical limitations that even AI-supported systems cannot solve?
  • How does a project to switch to AI-based visual inspection work?
  • What GMP authority requirements are there for such systems?
will be discussed and possible solutions presented.
 
 
(AI) Artificial Intelligence in Visual Inspection
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

(AI) Artificial Intelligence in a GxP Environment
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.
 
 
(AI) Artificial Intelligence in Visual Inspection
The target group for this event are specialists and managers in the pharmaceutical industry from the fields of engineering, production and quality assurance who are involved in the organisation or operation of visual inspection. This conference is also aimed at suppliers involved in the development and automation of inspection systems.

Technical Requirements

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Programme

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

Seminar Programme as PDF

Programme (AI) Artificial Intelligence in a GxP Environment - 29-30 October 2024
 
Introduction to Artificial Intelligence (AI)
  • History of AI
  • Types of AI
  • Real life examples
Introduction to Machine Learning (ML)
  • Technological basics
  • Different learning / training methods
  • Example use cases
AI in Image Processing
  • Introduction to deep learning models for imaging
  • Deep learning for diagnosis and prognosis
  • Pre-training of deep learning models
  • Explainable Artificial Intelligence
AI/ML in Pharma, Biotech, and Med Devices
  • Challenges for the Life science industry
  • The GAMP-perspective on AI/ML
  • Use cases / Known scenarios
Regulatory Requirements / Concerns / Assessment
  • Pharmaceutical laws
  • EU-GMP Guide Annex 11
  • Inspection strategy
  • What do inspectors expect from the regulated user?
Inspection Readiness
  • Overview of regulatory guidance and evolving inspection practices
  • Overview of supporting processes: data management, risk management, change management
  • Have documentation ready – provide reasoning and justifications
  • How to setup mock inspections successfully
Data and Models
  • Overview of model and data types
  • Data split: training, validation, testing
  • Data quality, representativeness and typical data challenges
  • Use of synthetic data
Introduction to and Application of Generative AI in Regulated Pharma
  • Introduction to Large Language Models (LLM) and example use cases
  • Specialization and tailoring of LLMs in computerized Systems
  • Typical risks when using LLMs
  • Performance evaluation and validation strategies for LLMs
Generative AI - Legal Requirements and practical Implementation
  • AI and law: introduction and overview
  • Outlook and update on the EU AI Act
  • Copyright and trade Secrets
  • Data protection requirements and processing of personal data
  • Practical implementation and best practicess
Validation Approaches
  • Maturity: Increasing autonomy and transferring Control
  • Governance: Developing and operating AI solutions in GxP-regulated areas
The Use of Artificial Intelligence in Pharmaceutical Manufacturing, Developments, Implementation, and Examples
  • Risks and Opportunities
  • Preparing the Quality Management System for AI
  • Where to implement and what to avoid
  • Pactical examples and attention Points
  • Validation requirements
  • Future Developments
Risk Management for AI/ML
  • Basics of a ML Risk and Control Framework
  • Applying QRM to development and operation of AI applications
  • Using hazard clusters to guide the risk process
Efficiency Increase in Pharma Production Lines through GMP-compliant AI Tools – Case Study Review
  • Lessons learned from primary and secondary packaging lines and Auto-Injectors production
  • When continuously learning and situationally acting tools can help and when not
  • Introduction of Use cases for live learning optimization tools in GMP-environment
  • Short Intro: AI-based behavior learning on high frequency machine data of whole production lines
  • Obstacles during validation
  • Learnings from 24/7-operations Integration
  • Review of realistic and unrealistic benefits
Trustworthy AI: Innovative Approaches for Transparency in Validation
  • Black box or partner: How can transparency of AI be increased?
  • Interpretability of AI-based decisions
  • Foundations of Explainable AI
  • Approaches for the validation of AI systems in GxP environment
 
Programme (AI) Artificial Intelligence in Visual Inspection - 31 October 2024
 
Reality & Opportunities of AI in the Industrial Environment
  • What is Artificial Intelligence?
  • Is GenAI a Game Changer?
  • Opportunities and risks
  • Industrial Use cases
Artificial Intelligence (AI) in Visual Inspection from a GMP Inspector‘s Perspective
  • Legal basis
  • GAMP® and AI (ML)
  • Validation
  • Operation and raw data
Application, Project Planning and Qualification of AI in fully automated Visual Inspection
  • Development of robust, reliable and production-ready models in 4 phases
  • Phase 1: Problem identification & description
  • Phase 2a: Specification of inspection concept
  • Phase 2b: Definition of the sample sets (artificial and production samples), creation of the datasets, clarification of the labelling strategy
  • Phase 3: Model design, training and verification - a risk-based approach
  • Phase 4: Qualification & validation
  • Processes & technologies
  • Technologies for efficient image data acquisition, variable model technologies, transfer learning / pre-trained models, labelling application
  • Documentation of model development: traceability, risk minimisation and build-up of confidence
Usage of AI in the Inspection of hard-to-inspect Container-Systems
  • Manual, semi-automated and fully-automated approaches
  • Use of Artificial Intelligence
  • Single chamber and multi-chamber bags
  • Inspection of Blow-Fill-Seal containers
  • Inspection of Form-Fill-Seal containers
  • General approach
  • Training and Machine Learning
  • Testing and Validation
  • Limitations

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

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