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Typical Applications
Worst-Case Method Validation
Demonstrates method capability against realistic defect types as part of a robust validation strategy.
New Packaging/Supplier Qualification
Systematically tests how different material combinations perform against relevant failure modes.
Process Optimization & Troubleshooting
Offers empirical evidence for fine-tuning equipment parameters like capping torque or filling alignment.
Training & Problem-Solving
Serves as hands-on tools for team training CCIT methods.
Core Value
Since real leaks are seldom perfect holes, regulators emphasize detecting "probable defects." Our positive controls address this need directly.
○ Real-World Relevance: Confirms your CCIT method can detect challenging, non-ideal defect types that pose actual risks, strengthening your validation package.
○ Process Risk Insight: Helps identify vulnerabilities in your packaging process. For example, simulators modeling "over-compressed stoppers" can verify the safety of capper settings.
○ Faster Root-Cause Analysis: Provides a comparative testing for investigating production leaks, accelerating troubleshooting and corrective actions.
Complete Solution
We support the custom development of defect simulators based on your specific failure history or concerns, helping you close the loop between testing data and production quality improvements.
More Information
How does ECA stipulate about visible particulates for injectable drugs?
4.3 What aspects should be considered when assessing artificially created test kits, and how should their suitability be justified?
Artificially created test kits are often used when representative production samples are unavailable, technically unsuitable, or cannot be obtained in sufficient quantity for qualification, training, or evaluation purposes.
Their suitability should be assessed based on the inspection technology used and the required level of representativeness. Artificially created defects and conforming units should exhibit optical, physical, and mechanical characteristics comparable to those of the real product under routine inspection conditions.
For AI-based inspection systems, higher requirements regarding representativeness may apply, as these systems often utilize very subtle features and characteristics to distinguish between acceptable and defective units. For example, a genuine lyophilized cake with crystalline reflective properties can generally be distinguished from a matte plaster-based imitation if these differences are visible in the image data.
If artificially created defects differ in relevant characteristics—such as texture, reflectivity, contrast, size, movement behaviour, or other optical or physical properties—there is a risk that the neural network learns features introduced by the artificial manufacturing process rather than the actual defect itself. Likewise, particle detection may be influenced by factors such as viscosity, turbidity, fill height particle size, or particle contrast. These factors should therefore be considered when assessing the suitability of artificially created test kits and may also affect the performance of conventional rule-based inspection systems.
Conventional rule-based systems are generally less susceptible to such effects because they evaluate predefined image features rather than learning complex representations. Although these approaches are often less selective and may be less suitable for highly challenging inspection tasks, they typically demonstrate greater robustness against variations or realism drifts within artificially created test kits.
Consequently, the suitability of artificially created test kits should always be justified and documented with regard to the inspection technology and the intended application. The decisive criterion is not whether defects are artificially created or taken from routine production, but whether the test kit reliably reproduces the behaviour of the final drug product under the selected inspection strategy.
Can one instrument test different package sizes?
Yes. Different package sizes and shapes can be tested by changing the test chamber, fixture or sample holder.
Can the system test pharmaceutical vials?
Yes. Vacuum decay testing can be configured for vials containing powder, lyophilized products or medicinal liquids. A package-specific chamber is normally required.
Why Choose Zholion?
Drive Process Efficiency
Use test data to optimize packaging operations, reducing waste and non-conformances.
Backed by Deep Expertise
Decades of specialization ensure we understand your integrity challenges and can provide practical solutions.
Designed for Compliance
Our products and guidance are aligned with regulatory expectations, helping you build a defensible quality system.