Main topic of this conference is the detection of defects like particles in injectables and their evaluation. Besides the current regulatory requirements with regards to particulate matter, routine 100% inspection of injectables will be addressed. Manual inspection as well as automated inspection systems will be covered, including validation, training, defect categories, AQL testing and trending.
Hintergrund
In most cases particles found in parenteral medicines will lead to a quarantined product or even to the recall of the product – as we have seen in the last years in the cases of several Pharmaceutical companies. Responsible staff in charge will have to start root cause analysis to find the source of the particles and will have to do an evaluation of batches already shipped.
There is still confusion within the global pharmaceutical Industry with regard to the requirements for testing for visible particles. After the USP chapters <790> and <1790> were published, things have become much clearer, at least for the US. In Europe chapter 5.17.2 of the European Pharmacopoeia now also gives further advice. However, many questions remain, e.g. concerning training, re-testing, detection capabilities and revalidation of inspection systems.
Furthermore, there has been a recognisable trend towards automated inspection machines throughout the last years. High expectations are also placed on the use of artificial intelligence. The challenge for pharmaceutical companies is to find a suitable machine for their products and to determine reasonable inspection parameters during qualification and validation. But also during routine process there are questions arising like re-testing and the usage of test-sets, doing AQL-Testing as well as the Adjustment of parameters of the vision system.
We will address those topics during the conference and discuss and answer questions on
The latest compendial requirements concerning particulate matter (EU & US)
How to comply with FDA’s new guidance on Visual Inspection
Compliance with the new EU Annex 1
Implementation of artificial intelligence in the automated inspection process
Reduction of false rejects in automated inspection Systems
How to inspect hard-to-inspect containers
How to set up a company-wide particle reduction Programme
How to transfer a visual inspection process during a site change
The fundamentals, such as training of operators in Manual inspection, AQL testing, trending and the validation of an AVI System are content of the Pre-Conference course on November 5th.
Zielgruppe
This course is directed at staff from sterile operations involved in the 100% inspection process, that is production, quality and engineering. But also suppliers of primary packaging materials and inspections technology are target group of this event.
All participants receive the current version of ECA’s Best Practice Paper on “Visual Inspection” for free.
Regulatory Requirements for the Visual Inspection of Parenterals
Compendial requirements
100% visual inspection & AQL testing
PharmEur, USP, JP - similarities and differences
News from the Annex I
Risk management considerations
Current FDA Requirements for Visual Inspection of Injectables (remote presentation)
Difference between current FDA and EU requirements
Use of quality risk management for the prevention of visible particulate contamination
FDA Visible Particulate case studies
Drug-Device combination product considerations
Visual Inspection of Parenterals – a GMP Inspector's View
Applicable regulations & guidance
Current requirements for pharmaceutical Industry
Expectations of a GMP-Inspector
Examples of observations
Presentation of the new Version 4.0 of the ECA Guide The new version 4.0 of the ECA guide has recently been released. The presentation will introduce the guide itself and the changes in the new version. Among other things a chapter on semi-automated visual inspection has been added. Also, the description of uninterrupted inspection times & breaks in manual visual inspection has been adapted. And the Annex 1 requirement for ‘performance checks at regular intervals throughout the batch’ for AV inspection was taken into account.
Semiautomated Inspection – Advantages and Risks
Regulations for the use of SAVI
Technical setup of a SAVI System
Differences manual and semi-automated inspection
Qualification of inspection personal
Typical inspection errors and walkarounds
Practical Approaches for the Inspection of hard-to inspect Container Systems Part I
Inspection of Bags
Single chamber and multi-chamber bags
Inspection of Blow-Fill-Seal containers
Inspection of Form-Fill-Seal containers
Manual, semi-automated and fully-automated approaches
Two step inspection
Part II
Use of artificial intelligence
General Approach
Training and Machine Learning
Testing and Validation
Limitations
Particle Lifecycle Concept
What does it tell about the product?
What can we learn about the visual inspection process and operator qualification?
Challenges and first experiences
Transfer of a Visual Inspection Process
Manual inspection
Manual process to manual process (same manufacturing location)
Manual process to manual process (different manufacturing location)
Test set transfer?
Create new test set? How, why?
What does validation look like?
End goals and success (what are we trying to achieve?)
Automated inspection
Automated transfer: same machine (pitfalls)
Automated transfer: different machine (pitfalls)
End goals and success (what are we trying to achieve?)
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 / pretrained models, labelling application
Documentation of model development: traceability, risk minimisation and build-up of confidence
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