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Home > All Products > JVT Article: Application of Probability of Passing Multiple Stage Tests in Benchmarking and Validation of Processes
JVT Article: Application of Probability of Passing Multiple Stage Tests in Benchmarking and Validation of Processes
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Item Number: JA0Q8074
By Pramote Cholayudth
INTRODUCTION
The three basic principles of quality assurance (QA) in the Food and Drug Administration (FDA) and World Health Organization (WHO) validation guidelines may be reproduced as follows:
1. Quality, safety, and efficacy must be designed and built into the product.
2. Quality cannot be inspected or tested into the product.
3. Each step of the manufacturing process must be controlled to maximize the probability that the finished product meets all quality and design specifications.
This article will discuss the scientific aspect of the last QA principle mentioned above and describe how to demonstrate the probability of meeting the product specifications, for multiple stage tests, for future quality control (QC) samples using process optimization or validation test results. Demonstrating the probability will have the following benefits:
• Providing a scientifically predictive tool for future samples without re-sampling or re-testing
• Evaluating the product quality level, and subsequently, the benchmarking of the manufacturing processes
• Providing the principle for establishing validation acceptance criteria
There is a direct correlation between product quality level and the probability of meeting a certain specification. For a high quality level, the probability level for a QC sample to meet the specification limit will be high or, in other words, will have a high percentage of QC samples (e.g., 90 to 95%), when taken in a large number, passing that particular test limit. Specification limits are normally based on those of the compendial monographs that include multiple stage tests. The tests have been established based on normality assumption resulting in a typical sampling plan with multiple individual dosage units – e.g., Content Uniformity. The assumption is the key statistical fundamental for computation of the probability of passing these multi-stage tests.
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